{smcl}
{com}{sf}{ul off}{txt}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\swhitt\Desktop\Mosul\Extrajudicial Killing Mosul\ISQ\Final\ISQ Extrajudicial replication logfile.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res}14 Feb 2021, 12:30:42

{com}. do "C:\Users\swhitt\Desktop\Mosul\Extrajudicial Killing Mosul\ISQ\Final\ISQ Extrajudicial replication dofile.do"
{txt}
{com}. *Manuscript Replication Instructions
. 
. *Manuscript text replication on ISIS support
. 
. *Although there may be self-censoring of responses, a total of 91% of IDP camp respondents indicated some form of ISIS affiliation to include having worked for ISIS (21%), fought for ISIS (17%), having family members who worked for ISIS (55%) and having family members who were put on trial for their role in ISIS (64%).
. 
. sum workedisis spouseisis famisis foughtisis spousefoughtisis famfoughtisis famtrialisis

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 2}workedisis {c |}{res}        195    .2102564    .4085396          0          1
{txt}{space 2}spouseisis {c |}{res}        195    .0358974    .1865133          0          1
{txt}{space 5}famisis {c |}{res}        195    .5487179    .4989018          0          1
{txt}{space 2}foughtisis {c |}{res}        195    .1692308    .3759208          0          1
{txt}spousefoug~s {c |}{res}        195    .1128205    .3171876          0          1
{txt}{hline 13}{c +}{hline 57}
famfoughti~s {c |}{res}        195          .2    .4010296          0          1
{txt}famtrialisis {c |}{res}        195    .6410256    .4809344          0          1
{txt}
{com}. 
. tab isisties

   {txt}additive {c |}
   index of {c |}
  ISIS ties {c |}
      (a-g) {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}         17        8.72        8.72
{txt}          1 {c |}{res}        178       91.28      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        195      100.00
{txt}
{com}. *(note: isisties coded 1 if any of the previous ISIS related variables were answered positively, 0 if otherwise)
. 
. *Figure 1
. 
. cibar revtrustgov, over1(mosul) 
{res}{txt}
{com}. graph save g1, replace
{res}{txt}(file g1.gph saved)

{com}. cibar revsupportisis, over1(mosul) 
{res}{txt}
{com}. graph save g2, replace
{res}{txt}(file g2.gph saved)

{com}. graph combine g1.gph g2.gph
{res}{txt}
{com}. *(Note: additional formatting required)
. 
. *Results – Manuscript discussion in text
. 
. *Consistent with H2, two sample t-tests indicate that the average punishment in the revenge treatment is significantly greater than in the security treatment (t =6.11, p<0.0000). The average punishment ranges between a short and long-term prison sentence in the revenge treatment (mean = 2.58, sd = 1.14) compared to short-term prison in the security threat treatment (mean = 1.98, sd =1.23).
. 
. ttest killrevthreat, by(treatment) unequal

{txt}Two-sample t test with unequal variances
{hline 9}{c TT}{hline 68}
   Group{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. Err.{col 47}Std. Dev.{col 59}[95% Conf. Interval]
{hline 9}{c +}{hline 68}
       1 {c |}{res}{col 12}    250{col 22}     2.58{col 34} .0720219{col 46} 1.138766{col 58}  2.43815{col 70}  2.72185
       {txt}2 {c |}{res}{col 12}    345{col 22} 1.982609{col 34} .0661865{col 46} 1.229359{col 58} 1.852428{col 70}  2.11279
{txt}{hline 9}{c +}{hline 68}
combined {c |}{res}{col 12}    595{col 22} 2.233613{col 34} .0503089{col 46} 1.227167{col 58} 2.134808{col 70} 2.332418
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 22} .5973913{col 34} .0978151{col 58}  .405261{col 70} .7895216
{txt}{hline 9}{c BT}{hline 68}
    diff = mean({res}1{txt}) - mean({res}2{txt})                                      t = {res}  6.1074
{txt}Ho: diff = 0                     Satterthwaite's degrees of freedom = {res} 558.721

    {txt}Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = {res}1.0000         {txt}Pr(|T| > |t|) = {res}0.0000          {txt}Pr(T > t) = {res}0.0000
{txt}
{com}. sum killrevthreat if treatment==1

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
killrevthr~t {c |}{res}        250        2.58    1.138766          1          5
{txt}
{com}. sum killrevthreat if treatment==2

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
killrevthr~t {c |}{res}        345    1.982609    1.229359          1          5
{txt}
{com}. 
. *In both subsamples, there is greater support for punishment in the grievance treatment than in the security threat treatment, but the treatment effect is much stronger among Mosul civilians (t =15.07, p<0.0000) than IDP camp respondents (t = 2.39, p<0.0096).
. 
. ttest killrevthreat if mosul==1, by(treatment) unequal

{txt}Two-sample t test with unequal variances
{hline 9}{c TT}{hline 68}
   Group{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. Err.{col 47}Std. Dev.{col 59}[95% Conf. Interval]
{hline 9}{c +}{hline 68}
       1 {c |}{res}{col 12}    200{col 22}    2.335{col 34} .0716908{col 46} 1.013861{col 58} 2.193629{col 70} 2.476371
       {txt}2 {c |}{res}{col 12}    201{col 22} 1.154229{col 34} .0316302{col 46} .4484356{col 58} 1.091857{col 70}   1.2166
{txt}{hline 9}{c +}{hline 68}
combined {c |}{res}{col 12}    401{col 22} 1.743142{col 34} .0489633{col 46} .9804892{col 58} 1.646885{col 70}   1.8394
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 22} 1.180771{col 34} .0783584{col 58} 1.026509{col 70} 1.335033
{txt}{hline 9}{c BT}{hline 68}
    diff = mean({res}1{txt}) - mean({res}2{txt})                                      t = {res} 15.0688
{txt}Ho: diff = 0                     Satterthwaite's degrees of freedom = {res} 273.696

    {txt}Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = {res}1.0000         {txt}Pr(|T| > |t|) = {res}0.0000          {txt}Pr(T > t) = {res}0.0000
{txt}
{com}. ttest killrevthreat if mosul==0, by(treatment) unequal

{txt}Two-sample t test with unequal variances
{hline 9}{c TT}{hline 68}
   Group{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. Err.{col 47}Std. Dev.{col 59}[95% Conf. Interval]
{hline 9}{c +}{hline 68}
       1 {c |}{res}{col 12}     50{col 22}     3.56{col 34} .1542857{col 46} 1.090965{col 58} 3.249951{col 70} 3.870049
       {txt}2 {c |}{res}{col 12}    144{col 22} 3.138889{col 34}  .085127{col 46} 1.021524{col 58} 2.970619{col 70} 3.307159
{txt}{hline 9}{c +}{hline 68}
combined {c |}{res}{col 12}    194{col 22} 3.247423{col 34} .0756228{col 46} 1.053304{col 58} 3.098269{col 70} 3.396576
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 22} .4211111{col 34} .1762121{col 58} .0704918{col 70} .7717304
{txt}{hline 9}{c BT}{hline 68}
    diff = mean({res}1{txt}) - mean({res}2{txt})                                      t = {res}  2.3898
{txt}Ho: diff = 0                     Satterthwaite's degrees of freedom = {res} 80.8088

    {txt}Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = {res}0.9904         {txt}Pr(|T| > |t|) = {res}0.0192          {txt}Pr(T > t) = {res}0.0096
{txt}
{com}. 
. *In particular, Mosul civilians’ remarkably strong preferences for amnesty in the security treatment (87%) indicates solid support for informal retributive violence against suspected insurgents, constitutes a clear disregard for international laws and norms, and does not bode well for peace and security.
. 
. tab killrevthreat if mosul==1 & treatment==2

   {txt}27. Extrajudicial {c |}
          Killing (1 {c |}
 =executing revenge, {c |}
       2 = executing {c |}
             threat) {c |}      Freq.     Percent        Cum.
{hline 21}{c +}{hline 35}
             Amnesty {c |}{res}        175       87.06       87.06
{txt}Short term detention {c |}{res}         23       11.44       98.51
{txt} Long term detention {c |}{res}          1        0.50       99.00
{txt}       Life sentence {c |}{res}          2        1.00      100.00
{txt}{hline 21}{c +}{hline 35}
               Total {c |}{res}        201      100.00
{txt}
{com}. 
. *Figure 2
. 
. ciplot killrevthreat if mosul==1, by(treatment) 
{res}{txt}
{com}. graph save g1, replace
{res}{txt}(file g1.gph saved)

{com}. ciplot killrevthreat if mosul==0, by(treatment) 
{res}{txt}
{com}. graph save g2, replace
{res}{txt}(file g2.gph saved)

{com}. graph combine g2.gph g1.gph
{res}{txt}
{com}. *(Note: additional formatting required)
. 
. twoway (histogram killrevthreat if treatment==1, by(mosul) discrete percent) (histogram killrevthreat if treatment==2, by(mosul) discrete percent fcolor(none) lcolor(black))
{res}{txt}
{com}. *(Note: additional formatting required as well as combining of sub-graphs to create Figure 2)
. 
. *Table 1
. 
. ologit killrevthreat treatment, robust

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res: -856.3246}  
Iteration 1:{space 3}log pseudolikelihood = {res: -833.7724}  
Iteration 2:{space 3}log pseudolikelihood = {res:-833.69508}  
Iteration 3:{space 3}log pseudolikelihood = {res:-833.69506}  
{res}
{txt}Ordered logistic regression{col 49}Number of obs{col 67}= {res}       595
{txt}{col 49}Wald chi2({res}1{txt}){col 67}= {res}     44.91
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-833.69506{txt}{col 49}Pseudo R2{col 67}= {res}    0.0264

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}killrevthreat{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      z{col 47}   P>|z|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}treatment {c |}{col 15}{res}{space 2}-1.016695{col 27}{space 2} .1517137{col 38}{space 1}   -6.70{col 47}{space 3}0.000{col 55}{space 4}-1.314049{col 68}{space 3}-.7193416
{txt}{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
        /cut1 {c |}{col 15}{res}{space 2}-2.073972{col 27}{space 2} .2357184{col 55}{space 4}-2.535972{col 68}{space 3}-1.611973
{txt}        /cut2 {c |}{col 15}{res}{space 2}-1.121366{col 27}{space 2} .2265504{col 55}{space 4}-1.565397{col 68}{space 3}-.6773357
{txt}        /cut3 {c |}{col 15}{res}{space 2}  .179272{col 27}{space 2} .2274929{col 55}{space 4}-.2666059{col 68}{space 3}   .62515
{txt}        /cut4 {c |}{col 15}{res}{space 2}  1.26041{col 27}{space 2}  .252225{col 55}{space 4}  .766058{col 68}{space 3} 1.754762
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. ologit killrevthreat treatment mosul, robust

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res: -856.3246}  
Iteration 1:{space 3}log pseudolikelihood = {res:-668.28322}  
Iteration 2:{space 3}log pseudolikelihood = {res:-660.52455}  
Iteration 3:{space 3}log pseudolikelihood = {res:-660.48673}  
Iteration 4:{space 3}log pseudolikelihood = {res:-660.48672}  
{res}
{txt}Ordered logistic regression{col 49}Number of obs{col 67}= {res}       595
{txt}{col 49}Wald chi2({res}2{txt}){col 67}= {res}    285.41
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-660.48672{txt}{col 49}Pseudo R2{col 67}= {res}    0.2287

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}killrevthreat{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      z{col 47}   P>|z|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}treatment {c |}{col 15}{res}{space 2}-2.312548{col 27}{space 2} .1910739{col 38}{space 1}  -12.10{col 47}{space 3}0.000{col 55}{space 4}-2.687045{col 68}{space 3} -1.93805
{txt}{space 8}mosul {c |}{col 15}{res}{space 2}-3.623134{col 27}{space 2} .2225202{col 38}{space 1}  -16.28{col 47}{space 3}0.000{col 55}{space 4}-4.059266{col 68}{space 3}-3.187003
{txt}{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
        /cut1 {c |}{col 15}{res}{space 2}-6.902306{col 27}{space 2} .4382454{col 55}{space 4}-7.761251{col 68}{space 3}-6.043361
{txt}        /cut2 {c |}{col 15}{res}{space 2}-5.352895{col 27}{space 2} .3996062{col 55}{space 4}-6.136109{col 68}{space 3}-4.569682
{txt}        /cut3 {c |}{col 15}{res}{space 2}-3.499515{col 27}{space 2} .3810643{col 55}{space 4}-4.246387{col 68}{space 3}-2.752643
{txt}        /cut4 {c |}{col 15}{res}{space 2}-2.160229{col 27}{space 2} .3800453{col 55}{space 4}-2.905104{col 68}{space 3}-1.415354
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. ologit killrevthreat treatment revisisnoquit revviolencejust revwillofpeople revblameciv isisvictim female age education income professional laborer unemployed moved, robust

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-852.02333}  
Iteration 1:{space 3}log pseudolikelihood = {res:-710.17996}  
Iteration 2:{space 3}log pseudolikelihood = {res:-705.39307}  
Iteration 3:{space 3}log pseudolikelihood = {res:-705.38705}  
Iteration 4:{space 3}log pseudolikelihood = {res:-705.38705}  
{res}
{txt}Ordered logistic regression{col 49}Number of obs{col 67}= {res}       592
{txt}{col 49}Wald chi2({res}14{txt}){col 67}= {res}    228.26
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-705.38705{txt}{col 49}Pseudo R2{col 67}= {res}    0.1721

{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}    Robust
{col 1}  killrevthreat{col 17}{c |}      Coef.{col 29}   Std. Err.{col 41}      z{col 49}   P>|z|{col 57}     [95% Con{col 70}f. Interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}treatment {c |}{col 17}{res}{space 2}-1.942136{col 29}{space 2} .1990438{col 40}{space 1}   -9.76{col 49}{space 3}0.000{col 57}{space 4}-2.332255{col 70}{space 3}-1.552018
{txt}{space 2}revisisnoquit {c |}{col 17}{res}{space 2} .6880854{col 29}{space 2} .1346017{col 40}{space 1}    5.11{col 49}{space 3}0.000{col 57}{space 4} .4242709{col 70}{space 3} .9518999
{txt}revviolencejust {c |}{col 17}{res}{space 2}-.0435306{col 29}{space 2} .1017675{col 40}{space 1}   -0.43{col 49}{space 3}0.669{col 57}{space 4}-.2429913{col 70}{space 3}   .15593
{txt}revwillofpeople {c |}{col 17}{res}{space 2}-.4336886{col 29}{space 2} .0968813{col 40}{space 1}   -4.48{col 49}{space 3}0.000{col 57}{space 4}-.6235724{col 70}{space 3}-.2438048
{txt}{space 4}revblameciv {c |}{col 17}{res}{space 2}-.7629105{col 29}{space 2} .0986378{col 40}{space 1}   -7.73{col 49}{space 3}0.000{col 57}{space 4}-.9562372{col 70}{space 3}-.5695839
{txt}{space 5}isisvictim {c |}{col 17}{res}{space 2}-.0359277{col 29}{space 2} .0998237{col 40}{space 1}   -0.36{col 49}{space 3}0.719{col 57}{space 4}-.2315785{col 70}{space 3}  .159723
{txt}{space 9}female {c |}{col 17}{res}{space 2}-.1340322{col 29}{space 2} .2086161{col 40}{space 1}   -0.64{col 49}{space 3}0.521{col 57}{space 4}-.5429122{col 70}{space 3} .2748478
{txt}{space 12}age {c |}{col 17}{res}{space 2}-.0110191{col 29}{space 2} .0079094{col 40}{space 1}   -1.39{col 49}{space 3}0.164{col 57}{space 4}-.0265213{col 70}{space 3} .0044831
{txt}{space 6}education {c |}{col 17}{res}{space 2}-.0151984{col 29}{space 2} .1033882{col 40}{space 1}   -0.15{col 49}{space 3}0.883{col 57}{space 4}-.2178355{col 70}{space 3} .1874388
{txt}{space 9}income {c |}{col 17}{res}{space 2}-.0060698{col 29}{space 2} .1257827{col 40}{space 1}   -0.05{col 49}{space 3}0.962{col 57}{space 4}-.2525994{col 70}{space 3} .2404598
{txt}{space 3}professional {c |}{col 17}{res}{space 2}-.3062648{col 29}{space 2}  .290663{col 40}{space 1}   -1.05{col 49}{space 3}0.292{col 57}{space 4}-.8759538{col 70}{space 3} .2634243
{txt}{space 8}laborer {c |}{col 17}{res}{space 2} .0164948{col 29}{space 2} .2692468{col 40}{space 1}    0.06{col 49}{space 3}0.951{col 57}{space 4}-.5112192{col 70}{space 3} .5442088
{txt}{space 5}unemployed {c |}{col 17}{res}{space 2} .1321062{col 29}{space 2} .2652471{col 40}{space 1}    0.50{col 49}{space 3}0.618{col 57}{space 4}-.3877685{col 70}{space 3} .6519809
{txt}{space 10}moved {c |}{col 17}{res}{space 2}-.3106721{col 29}{space 2} .1912715{col 40}{space 1}   -1.62{col 49}{space 3}0.104{col 57}{space 4}-.6855574{col 70}{space 3} .0642132
{txt}{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
          /cut1 {c |}{col 17}{res}{space 2}-6.204954{col 29}{space 2} .9610615{col 57}{space 4}  -8.0886{col 70}{space 3}-4.321308
{txt}          /cut2 {c |}{col 17}{res}{space 2}-4.896777{col 29}{space 2} .9406118{col 57}{space 4}-6.740343{col 70}{space 3}-3.053212
{txt}          /cut3 {c |}{col 17}{res}{space 2}-3.189147{col 29}{space 2} .9305823{col 57}{space 4}-5.013055{col 70}{space 3} -1.36524
{txt}          /cut4 {c |}{col 17}{res}{space 2}-1.861088{col 29}{space 2} .9303563{col 57}{space 4}-3.684553{col 70}{space 3}-.0376228
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. *Footnote 8
. 
. *Only 7% of respondents strongly disagree that violence is ever justifiable, and the item is not significant predictor of punishment preferences in Table 1.
. 
. tab revviolencejust

 {txt}38.d violence is {c |}
  justifiable for {c |}
   certain causes {c |}      Freq.     Percent        Cum.
{hline 18}{c +}{hline 35}
Strongly disagree {c |}{res}         42        7.05        7.05
{txt}Somewhat disagree {c |}{res}        105       17.62       24.66
{txt}   Somewhat agree {c |}{res}        202       33.89       58.56
{txt}   Strongly agree {c |}{res}        247       41.44      100.00
{txt}{hline 18}{c +}{hline 35}
            Total {c |}{res}        596      100.00
{txt}
{com}. 
. *Results – Judicial Killing in text discussion
. 
. *When comparing basic treatment effects across this survey item, we find that subjects in the surrender treatment are less likely to see capital punishment as a fair outcome than those in the capture treatment (two-sample t-test = 10.48, p<0.0000). However, when interacting treatments with ISIS affiliation via Mosul vs. IDP camp comparisons, Figure 3 shows that the treatment effect only applies to the subsample of Mosul civilians (t =10.12, p<0.0000). There is no significant treatment effect for respondents in IDP camps outside Mosul (t = 0.05, p<0.4782).
. 
. ttest revrightcapsur, by(treatment) unequal

{txt}Two-sample t test with unequal variances
{hline 9}{c TT}{hline 68}
   Group{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. Err.{col 47}Std. Dev.{col 59}[95% Conf. Interval]
{hline 9}{c +}{hline 68}
       1 {c |}{res}{col 12}    248{col 22} 3.366935{col 34} .0699364{col 46} 1.101359{col 58} 3.229188{col 70} 3.504683
       {txt}2 {c |}{res}{col 12}    343{col 22} 2.367347{col 34} .0648751{col 46} 1.201503{col 58} 2.239743{col 70} 2.494951
{txt}{hline 9}{c +}{hline 68}
combined {c |}{res}{col 12}    591{col 22} 2.786802{col 34}  .051842{col 46} 1.260305{col 58} 2.684985{col 70} 2.888619
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 22} .9995885{col 34} .0953932{col 58} .8122142{col 70} 1.186963
{txt}{hline 9}{c BT}{hline 68}
    diff = mean({res}1{txt}) - mean({res}2{txt})                                      t = {res} 10.4786
{txt}Ho: diff = 0                     Satterthwaite's degrees of freedom = {res} 557.071

    {txt}Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = {res}1.0000         {txt}Pr(|T| > |t|) = {res}0.0000          {txt}Pr(T > t) = {res}0.0000
{txt}
{com}. ttest revrightcapsur if mosul==1, by(treatment) unequal

{txt}Two-sample t test with unequal variances
{hline 9}{c TT}{hline 68}
   Group{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. Err.{col 47}Std. Dev.{col 59}[95% Conf. Interval]
{hline 9}{c +}{hline 68}
       1 {c |}{res}{col 12}    200{col 22}    3.735{col 34} .0527998{col 46} .7467013{col 58} 3.630881{col 70} 3.839119
       {txt}2 {c |}{res}{col 12}    201{col 22} 2.751244{col 34} .0815829{col 46} 1.156638{col 58} 2.590371{col 70} 2.912117
{txt}{hline 9}{c +}{hline 68}
combined {c |}{res}{col 12}    401{col 22} 3.241895{col 34}  .054449{col 46}  1.09034{col 58} 3.134853{col 70} 3.348937
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 22} .9837562{col 34} .0971781{col 58} .7926147{col 70} 1.174898
{txt}{hline 9}{c BT}{hline 68}
    diff = mean({res}1{txt}) - mean({res}2{txt})                                      t = {res} 10.1232
{txt}Ho: diff = 0                     Satterthwaite's degrees of freedom = {res} 342.279

    {txt}Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = {res}1.0000         {txt}Pr(|T| > |t|) = {res}0.0000          {txt}Pr(T > t) = {res}0.0000
{txt}
{com}. ttest revrightcapsur if mosul==0, by(treatment) unequal

{txt}Two-sample t test with unequal variances
{hline 9}{c TT}{hline 68}
   Group{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. Err.{col 47}Std. Dev.{col 59}[95% Conf. Interval]
{hline 9}{c +}{hline 68}
       1 {c |}{res}{col 12}     48{col 22} 1.833333{col 34} .1468744{col 46} 1.017576{col 58}  1.53786{col 70} 2.128807
       {txt}2 {c |}{res}{col 12}    142{col 22} 1.823944{col 34} .0878717{col 46} 1.047112{col 58} 1.650227{col 70}  1.99766
{txt}{hline 9}{c +}{hline 68}
combined {c |}{res}{col 12}    190{col 22} 1.826316{col 34} .0752363{col 46}  1.03706{col 58} 1.677905{col 70} 1.974726
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 22} .0093897{col 34} .1711536{col 58}-.3310207{col 70} .3498001
{txt}{hline 9}{c BT}{hline 68}
    diff = mean({res}1{txt}) - mean({res}2{txt})                                      t = {res}  0.0549
{txt}Ho: diff = 0                     Satterthwaite's degrees of freedom = {res} 83.1179

    {txt}Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = {res}0.5218         {txt}Pr(|T| > |t|) = {res}0.9564          {txt}Pr(T > t) = {res}0.4782
{txt}
{com}. 
. *Figure 3 indicates that less than 10% of IDP camp respondents strongly believed that the judge made the right decision handing down the death penalty.
. 
. tab revrightcapsur if mosul==0

  {txt}31.b  Fadhil {c |}
  (1=captured, {c |}
2=surrendered) {c |}
    judge made {c |}
right decision {c |}
    (reversed) {c |}      Freq.     Percent        Cum.
{hline 15}{c +}{hline 35}
Definitely Not {c |}{res}        104       54.45       54.45
{txt}  Probably Not {c |}{res}         35       18.32       72.77
{txt}  Probably Yes {c |}{res}         34       17.80       90.58
{txt}Definitely Yes {c |}{res}         18        9.42      100.00
{txt}{hline 15}{c +}{hline 35}
         Total {c |}{res}        191      100.00
{txt}
{com}. 
. *Figure 3
. 
. ciplot revrightcapsur if mosul==1, by(treatment) 
{res}{txt}
{com}. graph save g1, replace
{res}{txt}(file g1.gph saved)

{com}. ciplot revrightcapsur if mosul==0, by(treatment) 
{res}{txt}
{com}. graph save g2, replace
{res}{txt}(file g2.gph saved)

{com}. graph combine g2.gph g1.gph
{res}{txt}
{com}. *(Note: additional formatting required)
. 
. twoway (histogram revrightcapsur if treatment==1, by(mosul) discrete percent) (histogram revrightcapsur if treatment==2, by(mosul) discrete percent fcolor(none) lcolor(black))
{res}{txt}
{com}. *(Note: additional formatting required as well as combining of sub-graphs to create Figure 3)
. 
. *Results - In text discussion
. 
. *Many IDP camp respondents indicate that they preferred either a long-term sentence (58%), a short-term prison sentence (24%) or even amnesty (15%) in lieu of the death penalty in both treatments. As an alternative to capital punishment, most Mosul civilians indicated they would accept a life sentence (79%) or long-term incarceration (13%) in the capture treatment. In the surrender treatment, a slim majority (54%) still support a life sentence, but nearly one-third (32%) would support long-term imprisonment as an alternative to death.
. 
. tab fadhildecision if mosul==0

  {txt}32 Fadhil Decision {c |}      Freq.     Percent        Cum.
{hline 21}{c +}{hline 35}
             Amnesty {c |}{res}         24       15.00       15.00
{txt}Short term detention {c |}{res}         38       23.75       38.75
{txt} Long term detention {c |}{res}         93       58.13       96.88
{txt}       Life sentence {c |}{res}          5        3.13      100.00
{txt}{hline 21}{c +}{hline 35}
               Total {c |}{res}        160      100.00
{txt}
{com}. tab fadhildecision if mosul==1

  {txt}32 Fadhil Decision {c |}      Freq.     Percent        Cum.
{hline 21}{c +}{hline 35}
             Amnesty {c |}{res}         18        4.49        4.49
{txt}Short term detention {c |}{res}         25        6.23       10.72
{txt} Long term detention {c |}{res}         91       22.69       33.42
{txt}       Life sentence {c |}{res}        267       66.58      100.00
{txt}{hline 21}{c +}{hline 35}
               Total {c |}{res}        401      100.00
{txt}
{com}. 
. 
. *Table 2
. 
. ologit revrightcapsur treatment, robust

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-743.33834}  
Iteration 1:{space 3}log pseudolikelihood = {res:-691.45333}  
Iteration 2:{space 3}log pseudolikelihood = {res:-691.01435}  
Iteration 3:{space 3}log pseudolikelihood = {res:-691.01392}  
Iteration 4:{space 3}log pseudolikelihood = {res:-691.01392}  
{res}
{txt}Ordered logistic regression{col 49}Number of obs{col 67}= {res}       591
{txt}{col 49}Wald chi2({res}1{txt}){col 67}= {res}     84.22
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-691.01392{txt}{col 49}Pseudo R2{col 67}= {res}    0.0704

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28}    Robust
{col 1}revrightcapsur{col 16}{c |}      Coef.{col 28}   Std. Err.{col 40}      z{col 48}   P>|z|{col 56}     [95% Con{col 69}f. Interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}treatment {c |}{col 16}{res}{space 2}-1.699034{col 28}{space 2} .1851367{col 39}{space 1}   -9.18{col 48}{space 3}0.000{col 56}{space 4}-2.061895{col 69}{space 3}-1.336173
{txt}{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
         /cut1 {c |}{col 16}{res}{space 2}-4.002838{col 28}{space 2}  .356664{col 56}{space 4}-4.701886{col 69}{space 3}-3.303789
{txt}         /cut2 {c |}{col 16}{res}{space 2} -3.11101{col 28}{space 2} .3341784{col 56}{space 4}-3.765987{col 69}{space 3}-2.456032
{txt}         /cut3 {c |}{col 16}{res}{space 2}-2.555287{col 28}{space 2} .3184392{col 56}{space 4}-3.179417{col 69}{space 3}-1.931158
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. ologit revrightcapsur treatment mosul, robust

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-743.33834}  
Iteration 1:{space 3}log pseudolikelihood = {res:-620.54914}  
Iteration 2:{space 3}log pseudolikelihood = {res: -618.8848}  
Iteration 3:{space 3}log pseudolikelihood = {res:-618.87879}  
Iteration 4:{space 3}log pseudolikelihood = {res:-618.87879}  
{res}
{txt}Ordered logistic regression{col 49}Number of obs{col 67}= {res}       591
{txt}{col 49}Wald chi2({res}2{txt}){col 67}= {res}    194.68
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-618.87879{txt}{col 49}Pseudo R2{col 67}= {res}    0.1674

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28}    Robust
{col 1}revrightcapsur{col 16}{c |}      Coef.{col 28}   Std. Err.{col 40}      z{col 48}   P>|z|{col 56}     [95% Con{col 69}f. Interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}treatment {c |}{col 16}{res}{space 2}-1.492674{col 28}{space 2} .1803803{col 39}{space 1}   -8.28{col 48}{space 3}0.000{col 56}{space 4}-1.846213{col 69}{space 3}-1.139136
{txt}{space 9}mosul {c |}{col 16}{res}{space 2} 2.136317{col 28}{space 2} .1883162{col 39}{space 1}   11.34{col 48}{space 3}0.000{col 56}{space 4} 1.767224{col 69}{space 3}  2.50541
{txt}{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
         /cut1 {c |}{col 16}{res}{space 2} -2.42138{col 28}{space 2} .3530912{col 56}{space 4}-3.113426{col 69}{space 3}-1.729334
{txt}         /cut2 {c |}{col 16}{res}{space 2}-1.351511{col 28}{space 2} .3370556{col 56}{space 4}-2.012128{col 69}{space 3}-.6908946
{txt}         /cut3 {c |}{col 16}{res}{space 2}-.6855253{col 28}{space 2} .3233934{col 56}{space 4}-1.319365{col 69}{space 3}-.0516859
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. ologit revrightcapsur treatment revisisnoquit revbiasedisis revdeathpenjust revblameciv isisvictim female age education income professional laborer unemployed moved, robust

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-735.05556}  
Iteration 1:{space 3}log pseudolikelihood = {res:-581.16943}  
Iteration 2:{space 3}log pseudolikelihood = {res:-577.70696}  
Iteration 3:{space 3}log pseudolikelihood = {res:-577.68715}  
Iteration 4:{space 3}log pseudolikelihood = {res:-577.68715}  
{res}
{txt}Ordered logistic regression{col 49}Number of obs{col 67}= {res}       587
{txt}{col 49}Wald chi2({res}14{txt}){col 67}= {res}    235.61
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-577.68715{txt}{col 49}Pseudo R2{col 67}= {res}    0.2141

{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}    Robust
{col 1} revrightcapsur{col 17}{c |}      Coef.{col 29}   Std. Err.{col 41}      z{col 49}   P>|z|{col 57}     [95% Con{col 70}f. Interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}treatment {c |}{col 17}{res}{space 2}-1.462029{col 29}{space 2} .1969028{col 40}{space 1}   -7.43{col 49}{space 3}0.000{col 57}{space 4}-1.847951{col 70}{space 3}-1.076106
{txt}{space 2}revisisnoquit {c |}{col 17}{res}{space 2}-.8531345{col 29}{space 2} .1295496{col 40}{space 1}   -6.59{col 49}{space 3}0.000{col 57}{space 4}-1.107047{col 70}{space 3}-.5992219
{txt}{space 2}revbiasedisis {c |}{col 17}{res}{space 2}-.4908489{col 29}{space 2} .1237577{col 40}{space 1}   -3.97{col 49}{space 3}0.000{col 57}{space 4}-.7334095{col 70}{space 3}-.2482884
{txt}revdeathpenjust {c |}{col 17}{res}{space 2} .1064718{col 29}{space 2} .0904464{col 40}{space 1}    1.18{col 49}{space 3}0.239{col 57}{space 4}   -.0708{col 70}{space 3} .2837436
{txt}{space 4}revblameciv {c |}{col 17}{res}{space 2}-.0154974{col 29}{space 2} .0957061{col 40}{space 1}   -0.16{col 49}{space 3}0.871{col 57}{space 4}-.2030779{col 70}{space 3}  .172083
{txt}{space 5}isisvictim {c |}{col 17}{res}{space 2}-.0276999{col 29}{space 2} .1442864{col 40}{space 1}   -0.19{col 49}{space 3}0.848{col 57}{space 4}-.3104961{col 70}{space 3} .2550962
{txt}{space 9}female {c |}{col 17}{res}{space 2}-.2574908{col 29}{space 2}  .260174{col 40}{space 1}   -0.99{col 49}{space 3}0.322{col 57}{space 4}-.7674224{col 70}{space 3} .2524409
{txt}{space 12}age {c |}{col 17}{res}{space 2} .0112817{col 29}{space 2} .0083563{col 40}{space 1}    1.35{col 49}{space 3}0.177{col 57}{space 4}-.0050964{col 70}{space 3} .0276597
{txt}{space 6}education {c |}{col 17}{res}{space 2}-.0506335{col 29}{space 2} .1268213{col 40}{space 1}   -0.40{col 49}{space 3}0.690{col 57}{space 4}-.2991988{col 70}{space 3} .1979318
{txt}{space 9}income {c |}{col 17}{res}{space 2} .4031739{col 29}{space 2} .1282973{col 40}{space 1}    3.14{col 49}{space 3}0.002{col 57}{space 4} .1517158{col 70}{space 3}  .654632
{txt}{space 3}professional {c |}{col 17}{res}{space 2}-.0292884{col 29}{space 2} .3002152{col 40}{space 1}   -0.10{col 49}{space 3}0.922{col 57}{space 4}-.6176993{col 70}{space 3} .5591225
{txt}{space 8}laborer {c |}{col 17}{res}{space 2} .0654433{col 29}{space 2} .3217486{col 40}{space 1}    0.20{col 49}{space 3}0.839{col 57}{space 4}-.5651723{col 70}{space 3} .6960589
{txt}{space 5}unemployed {c |}{col 17}{res}{space 2} .3211489{col 29}{space 2} .3208719{col 40}{space 1}    1.00{col 49}{space 3}0.317{col 57}{space 4}-.3077485{col 70}{space 3} .9500463
{txt}{space 10}moved {c |}{col 17}{res}{space 2} .0562684{col 29}{space 2} .2268639{col 40}{space 1}    0.25{col 49}{space 3}0.804{col 57}{space 4}-.3883766{col 70}{space 3} .5009134
{txt}{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
          /cut1 {c |}{col 17}{res}{space 2}-5.751786{col 29}{space 2} .8389327{col 57}{space 4}-7.396064{col 70}{space 3}-4.107508
{txt}          /cut2 {c |}{col 17}{res}{space 2}-4.571682{col 29}{space 2} .8306685{col 57}{space 4}-6.199763{col 70}{space 3}-2.943602
{txt}          /cut3 {c |}{col 17}{res}{space 2}-3.859645{col 29}{space 2} .8209353{col 57}{space 4}-5.468649{col 70}{space 3}-2.250642
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. *Footnote 11
. 
. *Few Iraqis (13%) strongly oppose the death penalty, while 80% agree that the death penalty can be justified underscoring widespread support for retributive violence in the judicial system. 
. 
. tab revdeathpenjust

   {txt}38.c the death {c |}
 penality is just {c |}
      for certain {c |}
           crimes {c |}      Freq.     Percent        Cum.
{hline 18}{c +}{hline 35}
Strongly disagree {c |}{res}         75       12.58       12.58
{txt}Somewhat disagree {c |}{res}         44        7.38       19.97
{txt}   Somewhat agree {c |}{res}        153       25.67       45.64
{txt}   Strongly agree {c |}{res}        324       54.36      100.00
{txt}{hline 18}{c +}{hline 35}
            Total {c |}{res}        596      100.00
{txt}
{com}. 
. 
. *Appendix Table 1. Summary of Variables
. 
. sum killrevthreat revrightcapsur revisisnoquit revviolencejust revwillofpeople revbiasedisis revdeathpenjust revblameciv isisvictim female age education income professional laborer unemployed student moved

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
killrevthr~t {c |}{res}        596    2.236577    1.228268          1          5
{txt}revrightca~r {c |}{res}        592    2.783784    1.261378          1          4
{txt}revisisnoq~t {c |}{res}        596    2.150483    .9864848          1          4
{txt}revviolenc~t {c |}{res}        596    3.097315    .9297652          1          4
{txt}revwillofp~e {c |}{res}        594    2.760943    1.029132          1          4
{txt}{hline 13}{c +}{hline 57}
revbiasedi~s {c |}{res}        593    2.804384    .8922608          1          4
{txt}revdeathpe~t {c |}{res}        596    3.218121    1.036838          1          4
{txt}{space 1}revblameciv {c |}{res}        596     2.63255    1.112593          1          4
{txt}{space 2}isisvictim {c |}{res}        596    4.11e-09    .8120216   -.833234   3.157621
{txt}{space 6}female {c |}{res}        596     .216443    .4121655          0          1
{txt}{hline 13}{c +}{hline 57}
{space 9}age {c |}{res}        596    34.71309    12.75456         18         72
{txt}{space 3}education {c |}{res}        595    2.606723    .9137678          1          4
{txt}{space 6}income {c |}{res}        596    2.130872    .9310624          1          4
{txt}professional {c |}{res}        596    .2567114    .4371857          0          1
{txt}{space 5}laborer {c |}{res}        596    .3305369    .4708016          0          1
{txt}{hline 13}{c +}{hline 57}
{space 2}unemployed {c |}{res}        596    .1694631    .3754756          0          1
{txt}{space 5}student {c |}{res}        596     .238255    .4263738          0          1
{txt}{space 7}moved {c |}{res}        596    .1795302     .384118          0          1
{txt}
{com}. 
. 
. *Online Appendix Material
. 
. *Index of Support for Execution Response Items
. 
. *Index construction
. 
. factor faircapsur rightcapsur justcapsur revrevengecapsur revfadhildecision
{txt}(obs=552)

Factor analysis/correlation{col 50}Number of obs    = {res}       552
{col 5}{txt}Method: principal factors{col 50}Retained factors =   {res}       3
{col 5}{txt}Rotation: (unrotated){col 50}Number of params =   {res}      10

{txt}{col 5}{hline 13}{c TT}{hline 60}
{col 5}     Factor  {c |} {ralign 12:Eigenvalue}   Difference        Proportion   Cumulative
{col 5}{hline 13}{c +}{hline 60}
{col 5}{ralign 11:Factor1}  {c |}{res}      2.79891      2.60803            0.9880       0.9880
{txt}{col 5}{ralign 11:Factor2}  {c |}{res}      0.19088      0.08886            0.0674       1.0553
{txt}{col 5}{ralign 11:Factor3}  {c |}{res}      0.10202      0.17573            0.0360       1.0913
{txt}{col 5}{ralign 11:Factor4}  {c |}{res}     -0.07371      0.11138           -0.0260       1.0653
{txt}{col 5}{ralign 11:Factor5}  {c |}{res}     -0.18509            .           -0.0653       1.0000
{txt}{col 5}{hline 13}{c BT}{hline 60}
{col 5}LR test: independent vs. saturated:  chi2({res}10{txt}) ={res} 1713.17{txt} Prob>chi2 ={res} 0.0000

{txt}Factor loadings (pattern matrix) and unique variances

{space 4}{hline 13}{c  TT}{hline 10}{hline 10}{hline 10}{c  TT}{hline 14}
{space 4}{space 0}{ralign 12:Variable}{space 1}{c |}{space 1}{ralign 8:Factor1}{space 1}{space 1}{ralign 8:Factor2}{space 1}{space 1}{ralign 8:Factor3}{space 1}{c |}{space 1}{ralign 12:Uniqueness}{space 1}
{space 4}{hline 13}{c   +}{hline 10}{hline 10}{hline 10}{c   +}{hline 14}
{space 4}{space 0}{ralign 12:faircapsur}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.9166}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.1672}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0941}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.1230}}}{space 1}
{space 4}{space 0}{ralign 12:rightcapsur}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.9427}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.1426}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.0093}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.0908}}}{space 1}
{space 4}{space 0}{ralign 12:justcapsur}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.6167}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0025}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.2555}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.5544}}}{space 1}
{space 4}{space 0}{ralign 12:revrevenge~r}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.6982}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.2139}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.1547}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.4429}}}{space 1}
{space 4}{space 0}{ralign 12:revfadhild~n}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.4497}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.3112}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.0621}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.6971}}}{space 1}
{space 4}{hline 13}{c  BT}{hline 10}{hline 10}{hline 10}{c  BT}{hline 14}

{com}. alpha faircapsur rightcapsur justcapsur revrevengecapsur revfadhildecision

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res} .7126176
{txt}Number of items in the scale:{col 34}{res}        5
{txt}Scale reliability coefficient:{col 34}{res}   0.8477
{txt}
{com}. *(Note: indices already created in dataset using the gen function)
. 
. graph bar faircapsur rightcapsur justcapsur revrevengecapsur revfadhildecision if mosul==0 
{res}{txt}
{com}. graph bar faircapsur rightcapsur justcapsur revrevengecapsur revfadhildecision if mosul==1
{res}{txt}
{com}. 
. *ISIS Surrender/Stop Fighting Index
. 
. factor amnesty pardonpows amnestylib neglib
{txt}(obs=400)

Factor analysis/correlation{col 50}Number of obs    = {res}       400
{col 5}{txt}Method: principal factors{col 50}Retained factors =   {res}       2
{col 5}{txt}Rotation: (unrotated){col 50}Number of params =   {res}       6

{txt}{col 5}{hline 13}{c TT}{hline 60}
{col 5}     Factor  {c |} {ralign 12:Eigenvalue}   Difference        Proportion   Cumulative
{col 5}{hline 13}{c +}{hline 60}
{col 5}{ralign 11:Factor1}  {c |}{res}      1.68187      1.63560            1.2336       1.2336
{txt}{col 5}{ralign 11:Factor2}  {c |}{res}      0.04627      0.22541            0.0339       1.2676
{txt}{col 5}{ralign 11:Factor3}  {c |}{res}     -0.17914      0.00649           -0.1314       1.1362
{txt}{col 5}{ralign 11:Factor4}  {c |}{res}     -0.18563            .           -0.1362       1.0000
{txt}{col 5}{hline 13}{c BT}{hline 60}
{col 5}LR test: independent vs. saturated:  chi2({res}6{txt})  ={res}  396.51{txt} Prob>chi2 ={res} 0.0000

{txt}Factor loadings (pattern matrix) and unique variances

{space 4}{hline 13}{c  TT}{hline 10}{hline 10}{c  TT}{hline 14}
{space 4}{space 0}{ralign 12:Variable}{space 1}{c |}{space 1}{ralign 8:Factor1}{space 1}{space 1}{ralign 8:Factor2}{space 1}{c |}{space 1}{ralign 12:Uniqueness}{space 1}
{space 4}{hline 13}{c   +}{hline 10}{hline 10}{c   +}{hline 14}
{space 4}{space 0}{ralign 12:amnesty}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.6985}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.0746}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.5065}}}{space 1}
{space 4}{space 0}{ralign 12:pardonpows}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.5338}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.1466}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.6936}}}{space 1}
{space 4}{space 0}{ralign 12:amnestylib}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.6945}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0838}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.5107}}}{space 1}
{space 4}{space 0}{ralign 12:neglib}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.6533}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.1104}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.5611}}}{space 1}
{space 4}{hline 13}{c  BT}{hline 10}{hline 10}{c  BT}{hline 14}

{com}. alpha amnesty pardonpows amnestylib neglib

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res} .3115576
{txt}Number of items in the scale:{col 34}{res}        4
{txt}Scale reliability coefficient:{col 34}{res}   0.7544
{txt}
{com}. factor fairtrial notorture nodeathpenalty lessprison protectisis protectiraqi financialaid
{txt}(obs=180)

Factor analysis/correlation{col 50}Number of obs    = {res}       180
{col 5}{txt}Method: principal factors{col 50}Retained factors =   {res}       4
{col 5}{txt}Rotation: (unrotated){col 50}Number of params =   {res}      21

{txt}{col 5}{hline 13}{c TT}{hline 60}
{col 5}     Factor  {c |} {ralign 12:Eigenvalue}   Difference        Proportion   Cumulative
{col 5}{hline 13}{c +}{hline 60}
{col 5}{ralign 11:Factor1}  {c |}{res}      2.90073      2.55921            1.0072       1.0072
{txt}{col 5}{ralign 11:Factor2}  {c |}{res}      0.34152      0.24750            0.1186       1.1258
{txt}{col 5}{ralign 11:Factor3}  {c |}{res}      0.09402      0.04075            0.0326       1.1585
{txt}{col 5}{ralign 11:Factor4}  {c |}{res}      0.05327      0.16179            0.0185       1.1770
{txt}{col 5}{ralign 11:Factor5}  {c |}{res}     -0.10852      0.06178           -0.0377       1.1393
{txt}{col 5}{ralign 11:Factor6}  {c |}{res}     -0.17030      0.06055           -0.0591       1.0802
{txt}{col 5}{ralign 11:Factor7}  {c |}{res}     -0.23084            .           -0.0802       1.0000
{txt}{col 5}{hline 13}{c BT}{hline 60}
{col 5}LR test: independent vs. saturated:  chi2({res}21{txt}) ={res}  433.55{txt} Prob>chi2 ={res} 0.0000

{txt}Factor loadings (pattern matrix) and unique variances

{space 4}{hline 13}{c  TT}{hline 10}{hline 10}{hline 10}{hline 10}{c  TT}{hline 14}
{space 4}{space 0}{ralign 12:Variable}{space 1}{c |}{space 1}{ralign 8:Factor1}{space 1}{space 1}{ralign 8:Factor2}{space 1}{space 1}{ralign 8:Factor3}{space 1}{space 1}{ralign 8:Factor4}{space 1}{c |}{space 1}{ralign 12:Uniqueness}{space 1}
{space 4}{hline 13}{c   +}{hline 10}{hline 10}{hline 10}{hline 10}{c   +}{hline 14}
{space 4}{space 0}{ralign 12:fairtrial}{space 1}{c |}{space 1}{ralign 8:{res:{sf: -0.3077}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.1225}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.2227}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.0287}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.8399}}}{space 1}
{space 4}{space 0}{ralign 12:notorture}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.6580}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.2515}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0423}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.1337}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.4841}}}{space 1}
{space 4}{space 0}{ralign 12:nodeathpen~y}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.7932}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.0929}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.1126}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0217}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.3490}}}{space 1}
{space 4}{space 0}{ralign 12:lessprison}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.5971}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.3130}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.0711}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0522}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.5377}}}{space 1}
{space 4}{space 0}{ralign 12:protectisis}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.7357}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0358}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.1115}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.1226}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.4300}}}{space 1}
{space 4}{space 0}{ralign 12:protectiraqi}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.7091}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.3162}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0057}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0205}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.3967}}}{space 1}
{space 4}{space 0}{ralign 12:financialaid}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.5859}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.2353}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.1115}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.1262}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.5730}}}{space 1}
{space 4}{hline 13}{c  BT}{hline 10}{hline 10}{hline 10}{hline 10}{c  BT}{hline 14}

{com}. alpha fairtrial notorture nodeathpenalty lessprison protectisis protectiraqi financialaid

{txt}Test scale = mean(unstandardized items)
Reversed item: {res: fairtrial}

Average interitem covariance:{col 34}{res} .2341728
{txt}Number of items in the scale:{col 34}{res}        7
{txt}Scale reliability coefficient:{col 34}{res}   0.8188
{txt}
{com}. *(Note: indices already created in dataset using the gen function)
. 
. *Victimization Index
. 
. graph bar punishedis familypunishedis injuredis familyinjuredis familykilledis imprisonedis fleehomeis hometakenis womenabusedis if mosul==1
{res}{txt}
{com}. graph bar punishedis familypunishedis injuredis familyinjuredis familykilledis imprisonedis fleehomeis hometakenis womenabusedis if mosul==0
{res}{txt}
{com}. 
. factor punishedis familypunishedis injuredis familyinjuredis familykilledis imprisonedis fleehomeis hometakenis womenabusedis
{txt}(obs=596)

Factor analysis/correlation{col 50}Number of obs    = {res}       596
{col 5}{txt}Method: principal factors{col 50}Retained factors =   {res}       5
{col 5}{txt}Rotation: (unrotated){col 50}Number of params =   {res}      35

{txt}{col 5}{hline 13}{c TT}{hline 60}
{col 5}     Factor  {c |} {ralign 12:Eigenvalue}   Difference        Proportion   Cumulative
{col 5}{hline 13}{c +}{hline 60}
{col 5}{ralign 11:Factor1}  {c |}{res}      1.29834      0.54391            0.7524       0.7524
{txt}{col 5}{ralign 11:Factor2}  {c |}{res}      0.75444      0.35373            0.4372       1.1896
{txt}{col 5}{ralign 11:Factor3}  {c |}{res}      0.40071      0.35703            0.2322       1.4219
{txt}{col 5}{ralign 11:Factor4}  {c |}{res}      0.04368      0.03494            0.0253       1.4472
{txt}{col 5}{ralign 11:Factor5}  {c |}{res}      0.00874      0.10054            0.0051       1.4522
{txt}{col 5}{ralign 11:Factor6}  {c |}{res}     -0.09180      0.07987           -0.0532       1.3990
{txt}{col 5}{ralign 11:Factor7}  {c |}{res}     -0.17166      0.07695           -0.0995       1.2996
{txt}{col 5}{ralign 11:Factor8}  {c |}{res}     -0.24861      0.01967           -0.1441       1.1555
{txt}{col 5}{ralign 11:Factor9}  {c |}{res}     -0.26828            .           -0.1555       1.0000
{txt}{col 5}{hline 13}{c BT}{hline 60}
{col 5}LR test: independent vs. saturated:  chi2({res}36{txt}) ={res}  633.33{txt} Prob>chi2 ={res} 0.0000

{txt}Factor loadings (pattern matrix) and unique variances

{space 4}{hline 13}{c  TT}{hline 10}{hline 10}{hline 10}{hline 10}{hline 10}{c  TT}{hline 14}
{space 4}{space 0}{ralign 12:Variable}{space 1}{c |}{space 1}{ralign 8:Factor1}{space 1}{space 1}{ralign 8:Factor2}{space 1}{space 1}{ralign 8:Factor3}{space 1}{space 1}{ralign 8:Factor4}{space 1}{space 1}{ralign 8:Factor5}{space 1}{c |}{space 1}{ralign 12:Uniqueness}{space 1}
{space 4}{hline 13}{c   +}{hline 10}{hline 10}{hline 10}{hline 10}{hline 10}{c   +}{hline 14}
{space 4}{space 0}{ralign 12:punishedis}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.3398}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.4775}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.1422}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0247}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.0141}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.6355}}}{space 1}
{space 4}{space 0}{ralign 12:familypuni~s}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.2723}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.4101}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.1536}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.0832}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0280}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.7264}}}{space 1}
{space 4}{space 0}{ralign 12:injuredis}{space 1}{c |}{space 1}{ralign 8:{res:{sf: -0.1843}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.1268}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.2567}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.1292}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.0025}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.8674}}}{space 1}
{space 4}{space 0}{ralign 12:familyinju~s}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.2058}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.0537}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.3332}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.0159}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0465}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.8413}}}{space 1}
{space 4}{space 0}{ralign 12:familykill~s}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.2273}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.0640}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.3169}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0575}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.0500}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.8380}}}{space 1}
{space 4}{space 0}{ralign 12:imprisonedis}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.3036}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.1660}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.0723}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.1040}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.0377}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.8628}}}{space 1}
{space 4}{space 0}{ralign 12:fleehomeis}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.6125}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.3829}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0616}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0037}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0267}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.4738}}}{space 1}
{space 4}{space 0}{ralign 12:hometakenis}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.6455}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.3271}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0992}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.0177}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.0209}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.4658}}}{space 1}
{space 4}{space 0}{ralign 12:womenabuse~s}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.3111}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.2325}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.2463}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0689}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0227}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.7833}}}{space 1}
{space 4}{hline 13}{c  BT}{hline 10}{hline 10}{hline 10}{hline 10}{hline 10}{c  BT}{hline 14}

{com}. *(Note: index generated already in dataset using the predict command)
. 
. *Treatment Effects Estimation
. 
. *Balance tests
. 
. ksmirnov female if mosul==1, by(treatment)

{txt}Two-sample Kolmogorov-Smirnov test for equality of distribution functions

 Smaller group       D       P-value  
 {hline 35}
 1:                {res}  0.0000    1.000
{txt} 2:                {res} -0.0461    0.653
{txt} Combined K-S:     {res}  0.0461    0.984

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 401 observations.

{com}. ksmirnov female if mosul==0, by(treatment)

{txt}Two-sample Kolmogorov-Smirnov test for equality of distribution functions

 Smaller group       D       P-value  
 {hline 35}
 1:                {res}  0.0475    0.846
{txt} 2:                {res}  0.0000    1.000
{txt} Combined K-S:     {res}  0.0475    1.000

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 194 observations.

{com}. ksmirnov age if mosul==1, by(treatment)

{txt}Two-sample Kolmogorov-Smirnov test for equality of distribution functions

 Smaller group       D       P-value  
 {hline 35}
 1:                {res}  0.0503    0.602
{txt} 2:                {res} -0.0919    0.184
{txt} Combined K-S:     {res}  0.0919    0.365

{txt}Note: Ties exist in combined dataset;
      there are 48 unique values out of 401 observations.

{com}. ksmirnov age if mosul==0, by(treatment)

{txt}Two-sample Kolmogorov-Smirnov test for equality of distribution functions

 Smaller group       D       P-value  
 {hline 35}
 1:                {res}  0.2417    0.013
{txt} 2:                {res} -0.0192    0.973
{txt} Combined K-S:     {res}  0.2417    0.026

{txt}Note: Ties exist in combined dataset;
      there are 47 unique values out of 194 observations.

{com}. ksmirnov education if mosul==1, by(treatment)

{txt}Two-sample Kolmogorov-Smirnov test for equality of distribution functions

 Smaller group       D       P-value  
 {hline 35}
 1:                {res}  0.0000    1.000
{txt} 2:                {res} -0.1574    0.007
{txt} Combined K-S:     {res}  0.1574    0.014

{txt}Note: Ties exist in combined dataset;
      there are 4 unique values out of 400 observations.

{com}. ksmirnov education if mosul==0, by(treatment)

{txt}Two-sample Kolmogorov-Smirnov test for equality of distribution functions

 Smaller group       D       P-value  
 {hline 35}
 1:                {res}  0.1342    0.263
{txt} 2:                {res} -0.0011    1.000
{txt} Combined K-S:     {res}  0.1342    0.516

{txt}Note: Ties exist in combined dataset;
      there are 3 unique values out of 194 observations.

{com}. ksmirnov income if mosul==1, by(treatment)

{txt}Two-sample Kolmogorov-Smirnov test for equality of distribution functions

 Smaller group       D       P-value  
 {hline 35}
 1:                {res}  0.0000    1.000
{txt} 2:                {res} -0.1273    0.039
{txt} Combined K-S:     {res}  0.1273    0.077

{txt}Note: Ties exist in combined dataset;
      there are 4 unique values out of 401 observations.

{com}. ksmirnov income if mosul==0, by(treatment)

{txt}Two-sample Kolmogorov-Smirnov test for equality of distribution functions

 Smaller group       D       P-value  
 {hline 35}
 1:                {res}  0.0625    0.748
{txt} 2:                {res} -0.0594    0.769
{txt} Combined K-S:     {res}  0.0625    0.999

{txt}Note: Ties exist in combined dataset;
      there are 4 unique values out of 194 observations.

{com}. ksmirnov professional if mosul==1, by(treatment)

{txt}Two-sample Kolmogorov-Smirnov test for equality of distribution functions

 Smaller group       D       P-value  
 {hline 35}
 1:                {res}  0.0000    1.000
{txt} 2:                {res} -0.1114    0.083
{txt} Combined K-S:     {res}  0.1114    0.166

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 401 observations.

{com}. ksmirnov professional if mosul==0, by(treatment)

{txt}Two-sample Kolmogorov-Smirnov test for equality of distribution functions

 Smaller group       D       P-value  
 {hline 35}
 1:                {res}  0.0642    0.737
{txt} 2:                {res}  0.0000    1.000
{txt} Combined K-S:     {res}  0.0642    0.998

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 194 observations.

{com}. ksmirnov laborer if mosul==1, by(treatment)

{txt}Two-sample Kolmogorov-Smirnov test for equality of distribution functions

 Smaller group       D       P-value  
 {hline 35}
 1:                {res}  0.0588    0.500
{txt} 2:                {res}  0.0000    1.000
{txt} Combined K-S:     {res}  0.0588    0.878

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 401 observations.

{com}. ksmirnov laborer if mosul==0, by(treatment)

{txt}Two-sample Kolmogorov-Smirnov test for equality of distribution functions

 Smaller group       D       P-value  
 {hline 35}
 1:                {res}  0.0000    1.000
{txt} 2:                {res} -0.0167    0.980
{txt} Combined K-S:     {res}  0.0167    1.000

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 194 observations.

{com}. ksmirnov unemployed if mosul==1, by(treatment)

{txt}Two-sample Kolmogorov-Smirnov test for equality of distribution functions

 Smaller group       D       P-value  
 {hline 35}
 1:                {res}  0.0541    0.557
{txt} 2:                {res}  0.0000    1.000
{txt} Combined K-S:     {res}  0.0541    0.931

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 401 observations.

{com}. ksmirnov unemployed if mosul==0, by(treatment)

{txt}Two-sample Kolmogorov-Smirnov test for equality of distribution functions

 Smaller group       D       P-value  
 {hline 35}
 1:                {res}  0.0000    1.000
{txt} 2:                {res} -0.0464    0.852
{txt} Combined K-S:     {res}  0.0464    1.000

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 194 observations.

{com}. ksmirnov moved if mosul==1, by(treatment)

{txt}Two-sample Kolmogorov-Smirnov test for equality of distribution functions

 Smaller group       D       P-value  
 {hline 35}
 1:                {res}  0.1038    0.116
{txt} 2:                {res}  0.0000    1.000
{txt} Combined K-S:     {res}  0.1038    0.231

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 401 observations.

{com}. ksmirnov moved if mosul==0, by(treatment)

{txt}Two-sample Kolmogorov-Smirnov test for equality of distribution functions

 Smaller group       D       P-value  
 {hline 35}
 1:                {res}  0.0058    0.997
{txt} 2:                {res}  0.0000    1.000
{txt} Combined K-S:     {res}  0.0058    1.000

{txt}Note: Ties exist in combined dataset;
      there are 2 unique values out of 194 observations.

{com}. 
. *Treatment Effects
. 
. *Manuscript Table 1 Model 3
. 
. teffects ra (killrevthreat mosul revisisnoquit revviolencejust revwillofpeople revblameciv isisvictim female age education income professional laborer unemployed moved) (treatment)

{res}{txt}Iteration 0:{space 3}EE criterion = {res: 3.156e-29}  
Iteration 1:{space 3}EE criterion = {res: 5.488e-32}  
{res}
{txt}Treatment-effects estimation{col 49}Number of obs {col 67}= {res}       592
{txt:Estimator}{col 16}:{res: regression adjustment}
{txt:Outcome model}{col 16}:{res: linear}
{txt:Treatment model}{col 16}:{res: none}
{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}    Robust
{col 1}  killrevthreat{col 17}{c |}      Coef.{col 29}   Std. Err.{col 41}      z{col 49}   P>|z|{col 57}     [95% Con{col 70}f. Interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}ATE             {txt}{c |}
{space 6}treatment {c |}
{space 6}(2 vs 1)  {c |}{col 17}{res}{space 2} -.885493{col 29}{space 2} .0759848{col 40}{space 1}  -11.65{col 49}{space 3}0.000{col 57}{space 4} -1.03442{col 70}{space 3}-.7365656
{txt}{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}POmean          {txt}{c |}
{space 6}treatment {c |}
{space 13}1  {c |}{col 17}{res}{space 2}  2.68665{col 29}{space 2} .0699777{col 40}{space 1}   38.39{col 49}{space 3}0.000{col 57}{space 4} 2.549496{col 70}{space 3} 2.823804
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. teffects ipwra (killrevthreat mosul revisisnoquit revviolencejust revwillofpeople revblameciv isisvictim female age education income professional laborer unemployed moved) (treatment female age education income professional laborer unemployed moved)

{res}{txt}Iteration 0:{space 3}EE criterion = {res: 8.368e-18}  
Iteration 1:{space 3}EE criterion = {res: 5.570e-32}  
{res}
{txt}Treatment-effects estimation{col 49}Number of obs {col 67}= {res}       592
{txt:Estimator}{col 16}:{res: IPW regression adjustment}
{txt:Outcome model}{col 16}:{res: linear}
{txt:Treatment model}{col 16}:{res: logit}
{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}    Robust
{col 1}  killrevthreat{col 17}{c |}      Coef.{col 29}   Std. Err.{col 41}      z{col 49}   P>|z|{col 57}     [95% Con{col 70}f. Interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}ATE             {txt}{c |}
{space 6}treatment {c |}
{space 6}(2 vs 1)  {c |}{col 17}{res}{space 2} -.880982{col 29}{space 2} .0745606{col 40}{space 1}  -11.82{col 49}{space 3}0.000{col 57}{space 4}-1.027118{col 70}{space 3}-.7348459
{txt}{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}POmean          {txt}{c |}
{space 6}treatment {c |}
{space 13}1  {c |}{col 17}{res}{space 2} 2.684336{col 29}{space 2} .0682098{col 40}{space 1}   39.35{col 49}{space 3}0.000{col 57}{space 4} 2.550648{col 70}{space 3} 2.818025
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. teffects ipwra (killrevthreat) (treatment mosul female age education income professional laborer unemployed moved)

{res}{txt}Iteration 0:{space 3}EE criterion = {res: 2.424e-20}  
Iteration 1:{space 3}EE criterion = {res: 6.129e-32}  
{res}
{txt}Treatment-effects estimation{col 49}Number of obs {col 67}= {res}       594
{txt:Estimator}{col 16}:{res: IPW regression adjustment}
{txt:Outcome model}{col 16}:{res: linear}
{txt:Treatment model}{col 16}:{res: logit}
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}killrevthr~t{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}ATE          {txt}{c |}
{space 3}treatment {c |}
{space 3}(2 vs 1)  {c |}{col 14}{res}{space 2}-.9048599{col 26}{space 2} .0775214{col 37}{space 1}  -11.67{col 46}{space 3}0.000{col 54}{space 4}-1.056799{col 67}{space 3}-.7529206
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}POmean       {txt}{c |}
{space 3}treatment {c |}
{space 10}1  {c |}{col 14}{res}{space 2} 2.732567{col 26}{space 2} .0726069{col 37}{space 1}   37.64{col 46}{space 3}0.000{col 54}{space 4}  2.59026{col 67}{space 3} 2.874874
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. *Manuscript Table 2 Model 3
. 
. teffects ra (revalphacapsur mosul revisisnoquit revbiasedisis revdeathpenjust revblameciv isisvictim female age education income professional laborer unemployed moved) (treatment)

{res}{txt}Iteration 0:{space 3}EE criterion = {res: 5.614e-30}  
Iteration 1:{space 3}EE criterion = {res: 1.063e-31}  
{res}
{txt}Treatment-effects estimation{col 49}Number of obs {col 67}= {res}       591
{txt:Estimator}{col 16}:{res: regression adjustment}
{txt:Outcome model}{col 16}:{res: linear}
{txt:Treatment model}{col 16}:{res: none}
{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}    Robust
{col 1} revalphacapsur{col 17}{c |}      Coef.{col 29}   Std. Err.{col 41}      z{col 49}   P>|z|{col 57}     [95% Con{col 70}f. Interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}ATE             {txt}{c |}
{space 6}treatment {c |}
{space 6}(2 vs 1)  {c |}{col 17}{res}{space 2}-.5528984{col 29}{space 2} .0601099{col 40}{space 1}   -9.20{col 49}{space 3}0.000{col 57}{space 4}-.6707117{col 70}{space 3}-.4350851
{txt}{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}POmean          {txt}{c |}
{space 6}treatment {c |}
{space 13}1  {c |}{col 17}{res}{space 2} 3.174367{col 29}{space 2} .0488766{col 40}{space 1}   64.95{col 49}{space 3}0.000{col 57}{space 4} 3.078571{col 70}{space 3} 3.270163
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. teffects ipwra (revalphacapsur mosul revisisnoquit revbiasedisis revdeathpenjust revblameciv isisvictim female age education income professional laborer unemployed moved) (treatment female age education income professional laborer unemployed moved)

{res}{txt}Iteration 0:{space 3}EE criterion = {res: 4.864e-18}  
Iteration 1:{space 3}EE criterion = {res: 1.463e-31}  
{res}
{txt}Treatment-effects estimation{col 49}Number of obs {col 67}= {res}       591
{txt:Estimator}{col 16}:{res: IPW regression adjustment}
{txt:Outcome model}{col 16}:{res: linear}
{txt:Treatment model}{col 16}:{res: logit}
{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}    Robust
{col 1} revalphacapsur{col 17}{c |}      Coef.{col 29}   Std. Err.{col 41}      z{col 49}   P>|z|{col 57}     [95% Con{col 70}f. Interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}ATE             {txt}{c |}
{space 6}treatment {c |}
{space 6}(2 vs 1)  {c |}{col 17}{res}{space 2}-.5417539{col 29}{space 2} .0593303{col 40}{space 1}   -9.13{col 49}{space 3}0.000{col 57}{space 4}-.6580392{col 70}{space 3}-.4254686
{txt}{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}POmean          {txt}{c |}
{space 6}treatment {c |}
{space 13}1  {c |}{col 17}{res}{space 2} 3.162593{col 29}{space 2} .0478709{col 40}{space 1}   66.07{col 49}{space 3}0.000{col 57}{space 4} 3.068768{col 70}{space 3} 3.256418
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. teffects psmatch (revalphacapsur) (treatment mosul female age education income professional laborer unemployed moved)
{res}
{txt}Treatment-effects estimation{col 48}Number of obs {col 67}= {res}       594
{txt:Estimator}{col 16}:{res: propensity-score matching}{col 48}{txt:Matches: requested }{col 67}{txt:=}          1
{txt:Outcome model}{col 16}:{res: matching}{txt}{col 63}min {col 67}= {res}         1
{txt:Treatment model}{col 16}:{res: logit}{col 63}{txt:max }{col 67}{txt:=}          4
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}   AI Robust
{col 1}revalphaca~r{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}ATE          {txt}{c |}
{space 3}treatment {c |}
{space 3}(2 vs 1)  {c |}{col 14}{res}{space 2}-.5910634{col 26}{space 2} .0670436{col 37}{space 1}   -8.82{col 46}{space 3}0.000{col 54}{space 4}-.7224665{col 67}{space 3}-.4596603
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. *Mosul vs. Camp Sampling Locations Comparisons
. 
. logit mosul revisisnoquit revviolencejust revwillofpeople revblameciv isisvictim female age education income professional laborer unemployed moved, robust

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-374.13525}  
Iteration 1:{space 3}log pseudolikelihood = {res:-109.91827}  
Iteration 2:{space 3}log pseudolikelihood = {res:-83.803127}  
Iteration 3:{space 3}log pseudolikelihood = {res:-78.102884}  
Iteration 4:{space 3}log pseudolikelihood = {res:-77.938394}  
Iteration 5:{space 3}log pseudolikelihood = {res:-77.938234}  
Iteration 6:{space 3}log pseudolikelihood = {res:-77.938234}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}       593
{txt}{col 49}Wald chi2({res}13{txt}){col 67}= {res}    104.65
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-77.938234{txt}{col 49}Pseudo R2{col 67}= {res}    0.7917

{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}    Robust
{col 1}          mosul{col 17}{c |}      Coef.{col 29}   Std. Err.{col 41}      z{col 49}   P>|z|{col 57}     [95% Con{col 70}f. Interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}revisisnoquit {c |}{col 17}{res}{space 2}-2.878229{col 29}{space 2} .6109392{col 40}{space 1}   -4.71{col 49}{space 3}0.000{col 57}{space 4}-4.075648{col 70}{space 3} -1.68081
{txt}revviolencejust {c |}{col 17}{res}{space 2} .6824354{col 29}{space 2} .3898821{col 40}{space 1}    1.75{col 49}{space 3}0.080{col 57}{space 4}-.0817196{col 70}{space 3}  1.44659
{txt}revwillofpeople {c |}{col 17}{res}{space 2} .6407258{col 29}{space 2} .2131884{col 40}{space 1}    3.01{col 49}{space 3}0.003{col 57}{space 4} .2228842{col 70}{space 3} 1.058568
{txt}{space 4}revblameciv {c |}{col 17}{res}{space 2} 1.506014{col 29}{space 2} .2107338{col 40}{space 1}    7.15{col 49}{space 3}0.000{col 57}{space 4} 1.092983{col 70}{space 3} 1.919045
{txt}{space 5}isisvictim {c |}{col 17}{res}{space 2} -.151807{col 29}{space 2} .3226199{col 40}{space 1}   -0.47{col 49}{space 3}0.638{col 57}{space 4}-.7841304{col 70}{space 3} .4805164
{txt}{space 9}female {c |}{col 17}{res}{space 2}-1.491654{col 29}{space 2} .7976324{col 40}{space 1}   -1.87{col 49}{space 3}0.061{col 57}{space 4}-3.054985{col 70}{space 3} .0716766
{txt}{space 12}age {c |}{col 17}{res}{space 2} .0775417{col 29}{space 2} .0233358{col 40}{space 1}    3.32{col 49}{space 3}0.001{col 57}{space 4} .0318044{col 70}{space 3}  .123279
{txt}{space 6}education {c |}{col 17}{res}{space 2} .6891869{col 29}{space 2} .2673046{col 40}{space 1}    2.58{col 49}{space 3}0.010{col 57}{space 4} .1652795{col 70}{space 3} 1.213094
{txt}{space 9}income {c |}{col 17}{res}{space 2} 1.640522{col 29}{space 2} .5341301{col 40}{space 1}    3.07{col 49}{space 3}0.002{col 57}{space 4} .5936461{col 70}{space 3} 2.687398
{txt}{space 3}professional {c |}{col 17}{res}{space 2}-1.159442{col 29}{space 2} .7291866{col 40}{space 1}   -1.59{col 49}{space 3}0.112{col 57}{space 4}-2.588621{col 70}{space 3} .2697376
{txt}{space 8}laborer {c |}{col 17}{res}{space 2}-2.162226{col 29}{space 2} .8325654{col 40}{space 1}   -2.60{col 49}{space 3}0.009{col 57}{space 4}-3.794024{col 70}{space 3}-.5304278
{txt}{space 5}unemployed {c |}{col 17}{res}{space 2}-2.149206{col 29}{space 2} .6475537{col 40}{space 1}   -3.32{col 49}{space 3}0.001{col 57}{space 4}-3.418388{col 70}{space 3}-.8800241
{txt}{space 10}moved {c |}{col 17}{res}{space 2} .5433081{col 29}{space 2} .5758258{col 40}{space 1}    0.94{col 49}{space 3}0.345{col 57}{space 4}-.5852897{col 70}{space 3} 1.671906
{txt}{space 10}_cons {c |}{col 17}{res}{space 2}-5.317456{col 29}{space 2} 1.934316{col 40}{space 1}   -2.75{col 49}{space 3}0.006{col 57}{space 4}-9.108646{col 70}{space 3}-1.526266
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. logit mosul revisisnoquit revbiasedisis revdeathpenjust revblameciv isisvictim female age education income professional laborer unemployed moved, robust

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:  -373.011}  
Iteration 1:{space 3}log pseudolikelihood = {res:-96.197281}  
Iteration 2:{space 3}log pseudolikelihood = {res:-72.811966}  
Iteration 3:{space 3}log pseudolikelihood = {res:-58.864354}  
Iteration 4:{space 3}log pseudolikelihood = {res: -58.02525}  
Iteration 5:{space 3}log pseudolikelihood = {res:-58.016839}  
Iteration 6:{space 3}log pseudolikelihood = {res:-58.016834}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}       592
{txt}{col 49}Wald chi2({res}13{txt}){col 67}= {res}     53.04
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-58.016834{txt}{col 49}Pseudo R2{col 67}= {res}    0.8445

{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}    Robust
{col 1}          mosul{col 17}{c |}      Coef.{col 29}   Std. Err.{col 41}      z{col 49}   P>|z|{col 57}     [95% Con{col 70}f. Interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}revisisnoquit {c |}{col 17}{res}{space 2}-3.407867{col 29}{space 2} .8451532{col 40}{space 1}   -4.03{col 49}{space 3}0.000{col 57}{space 4}-5.064337{col 70}{space 3}-1.751398
{txt}{space 2}revbiasedisis {c |}{col 17}{res}{space 2} -1.20262{col 29}{space 2}  .287962{col 40}{space 1}   -4.18{col 49}{space 3}0.000{col 57}{space 4}-1.767015{col 70}{space 3}-.6382248
{txt}revdeathpenjust {c |}{col 17}{res}{space 2} 1.737233{col 29}{space 2} .4376812{col 40}{space 1}    3.97{col 49}{space 3}0.000{col 57}{space 4} .8793937{col 70}{space 3} 2.595072
{txt}{space 4}revblameciv {c |}{col 17}{res}{space 2} 1.712997{col 29}{space 2} .3131549{col 40}{space 1}    5.47{col 49}{space 3}0.000{col 57}{space 4} 1.099225{col 70}{space 3} 2.326769
{txt}{space 5}isisvictim {c |}{col 17}{res}{space 2}  .124555{col 29}{space 2} .4333952{col 40}{space 1}    0.29{col 49}{space 3}0.774{col 57}{space 4} -.724884{col 70}{space 3}  .973994
{txt}{space 9}female {c |}{col 17}{res}{space 2}-1.377889{col 29}{space 2} .8921055{col 40}{space 1}   -1.54{col 49}{space 3}0.122{col 57}{space 4}-3.126384{col 70}{space 3} .3706054
{txt}{space 12}age {c |}{col 17}{res}{space 2} .0853355{col 29}{space 2} .0245253{col 40}{space 1}    3.48{col 49}{space 3}0.001{col 57}{space 4} .0372669{col 70}{space 3} .1334042
{txt}{space 6}education {c |}{col 17}{res}{space 2} .8002171{col 29}{space 2} .3489578{col 40}{space 1}    2.29{col 49}{space 3}0.022{col 57}{space 4} .1162724{col 70}{space 3} 1.484162
{txt}{space 9}income {c |}{col 17}{res}{space 2} 1.109917{col 29}{space 2} .3635177{col 40}{space 1}    3.05{col 49}{space 3}0.002{col 57}{space 4} .3974359{col 70}{space 3} 1.822399
{txt}{space 3}professional {c |}{col 17}{res}{space 2}-1.590536{col 29}{space 2} .9466348{col 40}{space 1}   -1.68{col 49}{space 3}0.093{col 57}{space 4}-3.445906{col 70}{space 3} .2648342
{txt}{space 8}laborer {c |}{col 17}{res}{space 2}-2.796367{col 29}{space 2} 1.052961{col 40}{space 1}   -2.66{col 49}{space 3}0.008{col 57}{space 4}-4.860133{col 70}{space 3}-.7326007
{txt}{space 5}unemployed {c |}{col 17}{res}{space 2}-2.622638{col 29}{space 2} .8003174{col 40}{space 1}   -3.28{col 49}{space 3}0.001{col 57}{space 4}-4.191231{col 70}{space 3}-1.054045
{txt}{space 10}moved {c |}{col 17}{res}{space 2} 1.037433{col 29}{space 2} .6182777{col 40}{space 1}    1.68{col 49}{space 3}0.093{col 57}{space 4}-.1743694{col 70}{space 3} 2.249235
{txt}{space 10}_cons {c |}{col 17}{res}{space 2}-1.801859{col 29}{space 2} 1.832098{col 40}{space 1}   -0.98{col 49}{space 3}0.325{col 57}{space 4}-5.392705{col 70}{space 3} 1.788988
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. *Mosul vs. Camps on Likelihood of ISIS Surrender/Stop Fighting Index
. 
. cibar revisisnoquit, over1(mosul)
{res}{txt}
{com}. 
. twoway  (histogram revisisnoquit if mosul==0, percent) (histogram revisisnoquit if mosul==1, percent fcolor(none) lcolor(black)) 
{res}{txt}
{com}. *(Note: additional formatting required)
. 
. *Mosul vs. Camps on Blame Attribution
. 
. cibar revblameciv, over1(mosul)
{res}{txt}
{com}. 
. twoway  (histogram revblameciv if mosul==0, discrete percent) (histogram revblameciv if mosul==1, discrete percent fcolor(none) lcolor(black))
{res}{txt}
{com}. 
. *Mosul vs. Camps on other significant IVs in Manuscript Tables 1.
. 
. cibar revwillofpeople, over1(mosul)
{res}{txt}
{com}. 
. twoway  (histogram revwillofpeople if mosul==0, discrete percent) (histogram revwillofpeople if mosul==1, discrete percent fcolor(none) lcolor(black))
{res}{txt}
{com}. 
. *Mosul vs. Camps on other significant IVs in Manuscript Tables 2.
. 
. cibar revbiasedisis , over1(mosul)
{res}{txt}
{com}. 
. twoway  (histogram revbiasedisis if mosul==0, discrete percent) (histogram revbiasedisis if mosul==1,discrete percent fcolor(none) lcolor(black))
{res}{txt}
{com}. 
. cibar revdeathpenjust , over1(mosul)
{res}{txt}
{com}. 
. twoway  (histogram revdeathpenjust if mosul==0, discrete percent) (histogram revdeathpenjust if mosul==1,discrete percent fcolor(none) lcolor(black))
{res}{txt}
{com}. 
. *Manuscript Table 1 Robustness Checks
. 
. cibar killrevthreat, over1(treatment) over2(mosul)
{res}{txt}
{com}. *(Note: additional formatting required)
. 
. *Punishment for Extrajudicial Killing (OLS, Ordered Logit, Multinomial Logit Regression)
. 
. reg killrevthreat treatment, robust

{txt}Linear regression                               Number of obs     = {res}       595
                                                {txt}F(1, 593)         =  {res}    37.30
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0578
                                                {txt}Root MSE          =    {res} 1.1922

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}killrevthr~t{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}treatment {c |}{col 14}{res}{space 2}-.5973913{col 26}{space 2} .0978085{col 37}{space 1}   -6.11{col 46}{space 3}0.000{col 54}{space 4}-.7894846{col 67}{space 3} -.405298
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 3.177391{col 26}{space 2} .1584865{col 37}{space 1}   20.05{col 46}{space 3}0.000{col 54}{space 4} 2.866128{col 67}{space 3} 3.488655
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. ologit killrevthreat treatment, robust

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res: -856.3246}  
Iteration 1:{space 3}log pseudolikelihood = {res: -833.7724}  
Iteration 2:{space 3}log pseudolikelihood = {res:-833.69508}  
Iteration 3:{space 3}log pseudolikelihood = {res:-833.69506}  
{res}
{txt}Ordered logistic regression{col 49}Number of obs{col 67}= {res}       595
{txt}{col 49}Wald chi2({res}1{txt}){col 67}= {res}     44.91
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-833.69506{txt}{col 49}Pseudo R2{col 67}= {res}    0.0264

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}killrevthreat{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      z{col 47}   P>|z|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}treatment {c |}{col 15}{res}{space 2}-1.016695{col 27}{space 2} .1517137{col 38}{space 1}   -6.70{col 47}{space 3}0.000{col 55}{space 4}-1.314049{col 68}{space 3}-.7193416
{txt}{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
        /cut1 {c |}{col 15}{res}{space 2}-2.073972{col 27}{space 2} .2357184{col 55}{space 4}-2.535972{col 68}{space 3}-1.611973
{txt}        /cut2 {c |}{col 15}{res}{space 2}-1.121366{col 27}{space 2} .2265504{col 55}{space 4}-1.565397{col 68}{space 3}-.6773357
{txt}        /cut3 {c |}{col 15}{res}{space 2}  .179272{col 27}{space 2} .2274929{col 55}{space 4}-.2666059{col 68}{space 3}   .62515
{txt}        /cut4 {c |}{col 15}{res}{space 2}  1.26041{col 27}{space 2}  .252225{col 55}{space 4}  .766058{col 68}{space 3} 1.754762
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. mlogit killrevthreat treatment, robust

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res: -856.3246}  
Iteration 1:{space 3}log pseudolikelihood = {res:-819.08987}  
Iteration 2:{space 3}log pseudolikelihood = {res:-818.51154}  
Iteration 3:{space 3}log pseudolikelihood = {res:-818.51145}  
Iteration 4:{space 3}log pseudolikelihood = {res:-818.51145}  
{res}
{txt}Multinomial logistic regression{col 49}Number of obs{col 67}= {res}       595
{txt}{col 49}Wald chi2({res}4{txt}){col 67}= {res}     66.75
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-818.51145{txt}{col 49}Pseudo R2{col 67}= {res}    0.0442

{txt}{hline 21}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 22}{c |}{col 34}    Robust
{col 1}       killrevthreat{col 22}{c |}      Coef.{col 34}   Std. Err.{col 46}      z{col 54}   P>|z|{col 62}     [95% Con{col 75}f. Interval]
{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}Amnesty             {col 22}{txt}{c |}  (base outcome)
{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}Short_term_detention {txt}{c |}
{space 11}treatment {c |}{col 22}{res}{space 2}-1.753815{col 34}{space 2}    .2428{col 45}{space 1}   -7.22{col 54}{space 3}0.000{col 62}{space 4}-2.229694{col 75}{space 3}-1.277935
{txt}{space 15}_cons {c |}{col 22}{res}{space 2} 2.260376{col 34}{space 2} .4017653{col 45}{space 1}    5.63{col 54}{space 3}0.000{col 62}{space 4}  1.47293{col 75}{space 3} 3.047821
{txt}{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}Long_term_detention  {txt}{c |}
{space 11}treatment {c |}{col 22}{res}{space 2} -1.44633{col 34}{space 2} .2343779{col 45}{space 1}   -6.17{col 54}{space 3}0.000{col 62}{space 4}-1.905702{col 75}{space 3}-.9869576
{txt}{space 15}_cons {c |}{col 22}{res}{space 2}  1.91367{col 34}{space 2} .3986698{col 45}{space 1}    4.80{col 54}{space 3}0.000{col 62}{space 4} 1.132292{col 75}{space 3} 2.695049
{txt}{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}Life_sentence        {txt}{c |}
{space 11}treatment {c |}{col 22}{res}{space 2}-1.625981{col 34}{space 2} .3118781{col 45}{space 1}   -5.21{col 54}{space 3}0.000{col 62}{space 4}-2.237251{col 75}{space 3}-1.014711
{txt}{space 15}_cons {c |}{col 22}{res}{space 2} 1.272341{col 34}{space 2} .5022622{col 45}{space 1}    2.53{col 54}{space 3}0.011{col 62}{space 4} .2879252{col 75}{space 3} 2.256757
{txt}{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}Death_penalty        {txt}{c |}
{space 11}treatment {c |}{col 22}{res}{space 2}-1.237124{col 34}{space 2} .3721416{col 45}{space 1}   -3.32{col 54}{space 3}0.001{col 62}{space 4}-1.966508{col 75}{space 3}-.5077395
{txt}{space 15}_cons {c |}{col 22}{res}{space 2}  .220189{col 34}{space 2} .6157876{col 45}{space 1}    0.36{col 54}{space 3}0.721{col 62}{space 4}-.9867325{col 75}{space 3} 1.427111
{txt}{hline 21}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. *Punishment for Extrajudicial Killing (OLS, Ordered Logit, Multinomial Logit Regression, Controlling for Mosul vs. Camps)
. 
. reg killrevthreat treatment mosul, robust

{txt}Linear regression                               Number of obs     = {res}       595
                                                {txt}F(2, 592)         =  {res}   280.51
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.4769
                                                {txt}Root MSE          =    {res} .88907

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}killrevthr~t{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}treatment {c |}{col 14}{res}{space 2}-.9755222{col 26}{space 2} .0763232{col 37}{space 1}  -12.78{col 46}{space 3}0.000{col 54}{space 4}-1.125419{col 67}{space 3}-.8256249
{txt}{space 7}mosul {c |}{col 14}{res}{space 2}-1.739402{col 26}{space 2} .0848797{col 37}{space 1}  -20.49{col 46}{space 3}0.000{col 54}{space 4}-1.906104{col 67}{space 3}  -1.5727
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 4.947044{col 26}{space 2} .1577678{col 37}{space 1}   31.36{col 46}{space 3}0.000{col 54}{space 4} 4.637191{col 67}{space 3} 5.256897
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. ologit killrevthreat treatment mosul, robust

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res: -856.3246}  
Iteration 1:{space 3}log pseudolikelihood = {res:-668.28322}  
Iteration 2:{space 3}log pseudolikelihood = {res:-660.52455}  
Iteration 3:{space 3}log pseudolikelihood = {res:-660.48673}  
Iteration 4:{space 3}log pseudolikelihood = {res:-660.48672}  
{res}
{txt}Ordered logistic regression{col 49}Number of obs{col 67}= {res}       595
{txt}{col 49}Wald chi2({res}2{txt}){col 67}= {res}    285.41
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-660.48672{txt}{col 49}Pseudo R2{col 67}= {res}    0.2287

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}killrevthreat{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      z{col 47}   P>|z|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}treatment {c |}{col 15}{res}{space 2}-2.312548{col 27}{space 2} .1910739{col 38}{space 1}  -12.10{col 47}{space 3}0.000{col 55}{space 4}-2.687045{col 68}{space 3} -1.93805
{txt}{space 8}mosul {c |}{col 15}{res}{space 2}-3.623134{col 27}{space 2} .2225202{col 38}{space 1}  -16.28{col 47}{space 3}0.000{col 55}{space 4}-4.059266{col 68}{space 3}-3.187003
{txt}{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
        /cut1 {c |}{col 15}{res}{space 2}-6.902306{col 27}{space 2} .4382454{col 55}{space 4}-7.761251{col 68}{space 3}-6.043361
{txt}        /cut2 {c |}{col 15}{res}{space 2}-5.352895{col 27}{space 2} .3996062{col 55}{space 4}-6.136109{col 68}{space 3}-4.569682
{txt}        /cut3 {c |}{col 15}{res}{space 2}-3.499515{col 27}{space 2} .3810643{col 55}{space 4}-4.246387{col 68}{space 3}-2.752643
{txt}        /cut4 {c |}{col 15}{res}{space 2}-2.160229{col 27}{space 2} .3800453{col 55}{space 4}-2.905104{col 68}{space 3}-1.415354
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. mlogit killrevthreat treatment mosul, robust

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res: -856.3246}  
Iteration 1:{space 3}log pseudolikelihood = {res:-669.68017}  
Iteration 2:{space 3}log pseudolikelihood = {res:-646.08161}  
Iteration 3:{space 3}log pseudolikelihood = {res:-642.79205}  
Iteration 4:{space 3}log pseudolikelihood = {res:-642.77225}  
Iteration 5:{space 3}log pseudolikelihood = {res:-642.77225}  
{res}
{txt}Multinomial logistic regression{col 49}Number of obs{col 67}= {res}       595
{txt}{col 49}Wald chi2({res}8{txt}){col 67}= {res}    180.39
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-642.77225{txt}{col 49}Pseudo R2{col 67}= {res}    0.2494

{txt}{hline 21}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 22}{c |}{col 34}    Robust
{col 1}       killrevthreat{col 22}{c |}      Coef.{col 34}   Std. Err.{col 46}      z{col 54}   P>|z|{col 62}     [95% Con{col 75}f. Interval]
{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}Amnesty             {col 22}{txt}{c |}  (base outcome)
{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}Short_term_detention {txt}{c |}
{space 11}treatment {c |}{col 22}{res}{space 2}-2.550881{col 34}{space 2} .2959385{col 45}{space 1}   -8.62{col 54}{space 3}0.000{col 62}{space 4}-3.130909{col 75}{space 3}-1.970852
{txt}{space 15}mosul {c |}{col 22}{res}{space 2}-3.436794{col 34}{space 2} .4680367{col 45}{space 1}   -7.34{col 54}{space 3}0.000{col 62}{space 4}-4.354129{col 75}{space 3}-2.519459
{txt}{space 15}_cons {c |}{col 22}{res}{space 2} 6.450506{col 34}{space 2} .7391009{col 45}{space 1}    8.73{col 54}{space 3}0.000{col 62}{space 4} 5.001895{col 75}{space 3} 7.899117
{txt}{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}Long_term_detention  {txt}{c |}
{space 11}treatment {c |}{col 22}{res}{space 2}-3.677172{col 34}{space 2} .3805159{col 45}{space 1}   -9.66{col 54}{space 3}0.000{col 62}{space 4} -4.42297{col 75}{space 3}-2.931375
{txt}{space 15}mosul {c |}{col 22}{res}{space 2}-5.627847{col 34}{space 2} .5360474{col 45}{space 1}  -10.50{col 54}{space 3}0.000{col 62}{space 4}-6.678481{col 75}{space 3}-4.577214
{txt}{space 15}_cons {c |}{col 22}{res}{space 2} 9.427619{col 34}{space 2}  .915913{col 45}{space 1}   10.29{col 54}{space 3}0.000{col 62}{space 4} 7.632462{col 75}{space 3} 11.22278
{txt}{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}Life_sentence        {txt}{c |}
{space 11}treatment {c |}{col 22}{res}{space 2}-4.076049{col 34}{space 2} .5317905{col 45}{space 1}   -7.66{col 54}{space 3}0.000{col 62}{space 4}-5.118339{col 75}{space 3}-3.033759
{txt}{space 15}mosul {c |}{col 22}{res}{space 2}-5.989352{col 34}{space 2}  .660393{col 45}{space 1}   -9.07{col 54}{space 3}0.000{col 62}{space 4}-7.283698{col 75}{space 3}-4.695005
{txt}{space 15}_cons {c |}{col 22}{res}{space 2} 9.247065{col 34}{space 2} 1.137704{col 45}{space 1}    8.13{col 54}{space 3}0.000{col 62}{space 4} 7.017207{col 75}{space 3} 11.47692
{txt}{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}Death_penalty        {txt}{c |}
{space 11}treatment {c |}{col 22}{res}{space 2}-4.305009{col 34}{space 2} .5494947{col 45}{space 1}   -7.83{col 54}{space 3}0.000{col 62}{space 4}-5.381999{col 75}{space 3} -3.22802
{txt}{space 15}mosul {c |}{col 22}{res}{space 2}-7.454026{col 34}{space 2} .7578231{col 45}{space 1}   -9.84{col 54}{space 3}0.000{col 62}{space 4}-8.939332{col 75}{space 3} -5.96872
{txt}{space 15}_cons {c |}{col 22}{res}{space 2} 9.471224{col 34}{space 2}  1.15706{col 45}{space 1}    8.19{col 54}{space 3}0.000{col 62}{space 4} 7.203428{col 75}{space 3} 11.73902
{txt}{hline 21}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. *Punishment for Extrajudicial Killing (OLS, Ordered Logit, Multinomial Logit Regression, Extended Controls)
. 
. reg killrevthreat treatment revisisnoquit revviolencejust revwillofpeople revblameciv isisvictim female age education income professional laborer unemployed moved, robust

{txt}Linear regression                               Number of obs     = {res}       592
                                                {txt}F(14, 577)        =  {res}    32.57
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.3868
                                                {txt}Root MSE          =    {res} .97408

{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}    Robust
{col 1}  killrevthreat{col 17}{c |}      Coef.{col 29}   Std. Err.{col 41}      t{col 49}   P>|t|{col 57}     [95% Con{col 70}f. Interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}treatment {c |}{col 17}{res}{space 2}-.8869302{col 29}{space 2} .0879997{col 40}{space 1}  -10.08{col 49}{space 3}0.000{col 57}{space 4}-1.059769{col 70}{space 3}-.7140913
{txt}{space 2}revisisnoquit {c |}{col 17}{res}{space 2} .3595363{col 29}{space 2} .0636676{col 40}{space 1}    5.65{col 49}{space 3}0.000{col 57}{space 4} .2344878{col 70}{space 3} .4845848
{txt}revviolencejust {c |}{col 17}{res}{space 2}-.0352577{col 29}{space 2} .0499562{col 40}{space 1}   -0.71{col 49}{space 3}0.481{col 57}{space 4} -.133376{col 70}{space 3} .0628605
{txt}revwillofpeople {c |}{col 17}{res}{space 2}-.2252566{col 29}{space 2} .0467232{col 40}{space 1}   -4.82{col 49}{space 3}0.000{col 57}{space 4}-.3170249{col 70}{space 3}-.1334883
{txt}{space 4}revblameciv {c |}{col 17}{res}{space 2} -.403353{col 29}{space 2} .0471119{col 40}{space 1}   -8.56{col 49}{space 3}0.000{col 57}{space 4}-.4958847{col 70}{space 3}-.3108214
{txt}{space 5}isisvictim {c |}{col 17}{res}{space 2}-.0138623{col 29}{space 2} .0494297{col 40}{space 1}   -0.28{col 49}{space 3}0.779{col 57}{space 4}-.1109464{col 70}{space 3} .0832217
{txt}{space 9}female {c |}{col 17}{res}{space 2}-.0475557{col 29}{space 2} .0999588{col 40}{space 1}   -0.48{col 49}{space 3}0.634{col 57}{space 4}-.2438832{col 70}{space 3} .1487718
{txt}{space 12}age {c |}{col 17}{res}{space 2}-.0075611{col 29}{space 2} .0038495{col 40}{space 1}   -1.96{col 49}{space 3}0.050{col 57}{space 4}-.0151217{col 70}{space 3}-3.97e-07
{txt}{space 6}education {c |}{col 17}{res}{space 2} .0017658{col 29}{space 2} .0508101{col 40}{space 1}    0.03{col 49}{space 3}0.972{col 57}{space 4}-.0980295{col 70}{space 3} .1015611
{txt}{space 9}income {c |}{col 17}{res}{space 2} .0110022{col 29}{space 2} .0602147{col 40}{space 1}    0.18{col 49}{space 3}0.855{col 57}{space 4}-.1072644{col 70}{space 3} .1292689
{txt}{space 3}professional {c |}{col 17}{res}{space 2}-.0625102{col 29}{space 2} .1393684{col 40}{space 1}   -0.45{col 49}{space 3}0.654{col 57}{space 4}-.3362415{col 70}{space 3} .2112211
{txt}{space 8}laborer {c |}{col 17}{res}{space 2} .0461901{col 29}{space 2} .1313344{col 40}{space 1}    0.35{col 49}{space 3}0.725{col 57}{space 4}-.2117617{col 70}{space 3} .3041418
{txt}{space 5}unemployed {c |}{col 17}{res}{space 2} .1179198{col 29}{space 2} .1257156{col 40}{space 1}    0.94{col 49}{space 3}0.349{col 57}{space 4}-.1289962{col 70}{space 3} .3648358
{txt}{space 10}moved {c |}{col 17}{res}{space 2}-.1338887{col 29}{space 2} .0910702{col 40}{space 1}   -1.47{col 49}{space 3}0.142{col 57}{space 4}-.3127582{col 70}{space 3} .0449808
{txt}{space 10}_cons {c |}{col 17}{res}{space 2} 4.902931{col 29}{space 2} .4510462{col 40}{space 1}   10.87{col 49}{space 3}0.000{col 57}{space 4} 4.017038{col 70}{space 3} 5.788823
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. ologit killrevthreat treatment revisisnoquit revviolencejust revwillofpeople revblameciv isisvictim female age education income professional laborer unemployed moved, robust

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-852.02333}  
Iteration 1:{space 3}log pseudolikelihood = {res:-710.17996}  
Iteration 2:{space 3}log pseudolikelihood = {res:-705.39307}  
Iteration 3:{space 3}log pseudolikelihood = {res:-705.38705}  
Iteration 4:{space 3}log pseudolikelihood = {res:-705.38705}  
{res}
{txt}Ordered logistic regression{col 49}Number of obs{col 67}= {res}       592
{txt}{col 49}Wald chi2({res}14{txt}){col 67}= {res}    228.26
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-705.38705{txt}{col 49}Pseudo R2{col 67}= {res}    0.1721

{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}    Robust
{col 1}  killrevthreat{col 17}{c |}      Coef.{col 29}   Std. Err.{col 41}      z{col 49}   P>|z|{col 57}     [95% Con{col 70}f. Interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}treatment {c |}{col 17}{res}{space 2}-1.942136{col 29}{space 2} .1990438{col 40}{space 1}   -9.76{col 49}{space 3}0.000{col 57}{space 4}-2.332255{col 70}{space 3}-1.552018
{txt}{space 2}revisisnoquit {c |}{col 17}{res}{space 2} .6880854{col 29}{space 2} .1346017{col 40}{space 1}    5.11{col 49}{space 3}0.000{col 57}{space 4} .4242709{col 70}{space 3} .9518999
{txt}revviolencejust {c |}{col 17}{res}{space 2}-.0435306{col 29}{space 2} .1017675{col 40}{space 1}   -0.43{col 49}{space 3}0.669{col 57}{space 4}-.2429913{col 70}{space 3}   .15593
{txt}revwillofpeople {c |}{col 17}{res}{space 2}-.4336886{col 29}{space 2} .0968813{col 40}{space 1}   -4.48{col 49}{space 3}0.000{col 57}{space 4}-.6235724{col 70}{space 3}-.2438048
{txt}{space 4}revblameciv {c |}{col 17}{res}{space 2}-.7629105{col 29}{space 2} .0986378{col 40}{space 1}   -7.73{col 49}{space 3}0.000{col 57}{space 4}-.9562372{col 70}{space 3}-.5695839
{txt}{space 5}isisvictim {c |}{col 17}{res}{space 2}-.0359277{col 29}{space 2} .0998237{col 40}{space 1}   -0.36{col 49}{space 3}0.719{col 57}{space 4}-.2315785{col 70}{space 3}  .159723
{txt}{space 9}female {c |}{col 17}{res}{space 2}-.1340322{col 29}{space 2} .2086161{col 40}{space 1}   -0.64{col 49}{space 3}0.521{col 57}{space 4}-.5429122{col 70}{space 3} .2748478
{txt}{space 12}age {c |}{col 17}{res}{space 2}-.0110191{col 29}{space 2} .0079094{col 40}{space 1}   -1.39{col 49}{space 3}0.164{col 57}{space 4}-.0265213{col 70}{space 3} .0044831
{txt}{space 6}education {c |}{col 17}{res}{space 2}-.0151984{col 29}{space 2} .1033882{col 40}{space 1}   -0.15{col 49}{space 3}0.883{col 57}{space 4}-.2178355{col 70}{space 3} .1874388
{txt}{space 9}income {c |}{col 17}{res}{space 2}-.0060698{col 29}{space 2} .1257827{col 40}{space 1}   -0.05{col 49}{space 3}0.962{col 57}{space 4}-.2525994{col 70}{space 3} .2404598
{txt}{space 3}professional {c |}{col 17}{res}{space 2}-.3062648{col 29}{space 2}  .290663{col 40}{space 1}   -1.05{col 49}{space 3}0.292{col 57}{space 4}-.8759538{col 70}{space 3} .2634243
{txt}{space 8}laborer {c |}{col 17}{res}{space 2} .0164948{col 29}{space 2} .2692468{col 40}{space 1}    0.06{col 49}{space 3}0.951{col 57}{space 4}-.5112192{col 70}{space 3} .5442088
{txt}{space 5}unemployed {c |}{col 17}{res}{space 2} .1321062{col 29}{space 2} .2652471{col 40}{space 1}    0.50{col 49}{space 3}0.618{col 57}{space 4}-.3877685{col 70}{space 3} .6519809
{txt}{space 10}moved {c |}{col 17}{res}{space 2}-.3106721{col 29}{space 2} .1912715{col 40}{space 1}   -1.62{col 49}{space 3}0.104{col 57}{space 4}-.6855574{col 70}{space 3} .0642132
{txt}{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
          /cut1 {c |}{col 17}{res}{space 2}-6.204954{col 29}{space 2} .9610615{col 57}{space 4}  -8.0886{col 70}{space 3}-4.321308
{txt}          /cut2 {c |}{col 17}{res}{space 2}-4.896777{col 29}{space 2} .9406118{col 57}{space 4}-6.740343{col 70}{space 3}-3.053212
{txt}          /cut3 {c |}{col 17}{res}{space 2}-3.189147{col 29}{space 2} .9305823{col 57}{space 4}-5.013055{col 70}{space 3} -1.36524
{txt}          /cut4 {c |}{col 17}{res}{space 2}-1.861088{col 29}{space 2} .9303563{col 57}{space 4}-3.684553{col 70}{space 3}-.0376228
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. mlogit killrevthreat treatment revisisnoquit revviolencejust revwillofpeople revblameciv isisvictim female age education income professional laborer unemployed moved, robust

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-852.02333}  
Iteration 1:{space 3}log pseudolikelihood = {res:-672.30067}  
Iteration 2:{space 3}log pseudolikelihood = {res: -640.9941}  
Iteration 3:{space 3}log pseudolikelihood = {res:-638.79577}  
Iteration 4:{space 3}log pseudolikelihood = {res:-638.74738}  
Iteration 5:{space 3}log pseudolikelihood = {res:-638.74723}  
Iteration 6:{space 3}log pseudolikelihood = {res:-638.74723}  
{res}
{txt}Multinomial logistic regression{col 49}Number of obs{col 67}= {res}       592
{txt}{col 49}Wald chi2({res}56{txt}){col 67}= {res}    274.40
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-638.74723{txt}{col 49}Pseudo R2{col 67}= {res}    0.2503

{txt}{hline 21}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 22}{c |}{col 34}    Robust
{col 1}       killrevthreat{col 22}{c |}      Coef.{col 34}   Std. Err.{col 46}      z{col 54}   P>|z|{col 62}     [95% Con{col 75}f. Interval]
{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}Amnesty             {col 22}{txt}{c |}  (base outcome)
{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}Short_term_detention {txt}{c |}
{space 11}treatment {c |}{col 22}{res}{space 2}-2.343148{col 34}{space 2}   .33317{col 45}{space 1}   -7.03{col 54}{space 3}0.000{col 62}{space 4}-2.996149{col 75}{space 3}-1.690147
{txt}{space 7}revisisnoquit {c |}{col 22}{res}{space 2} .5440862{col 34}{space 2} .1957808{col 45}{space 1}    2.78{col 54}{space 3}0.005{col 62}{space 4} .1603628{col 75}{space 3} .9278095
{txt}{space 5}revviolencejust {c |}{col 22}{res}{space 2} .3724249{col 34}{space 2} .1558278{col 45}{space 1}    2.39{col 54}{space 3}0.017{col 62}{space 4} .0670081{col 75}{space 3} .6778418
{txt}{space 5}revwillofpeople {c |}{col 22}{res}{space 2}-.0384739{col 34}{space 2}  .126813{col 45}{space 1}   -0.30{col 54}{space 3}0.762{col 62}{space 4}-.2870229{col 75}{space 3} .2100751
{txt}{space 9}revblameciv {c |}{col 22}{res}{space 2}-.5732274{col 34}{space 2} .1483104{col 45}{space 1}   -3.87{col 54}{space 3}0.000{col 62}{space 4}-.8639105{col 75}{space 3}-.2825444
{txt}{space 10}isisvictim {c |}{col 22}{res}{space 2} .0422629{col 34}{space 2} .1530631{col 45}{space 1}    0.28{col 54}{space 3}0.782{col 62}{space 4}-.2577353{col 75}{space 3} .3422612
{txt}{space 14}female {c |}{col 22}{res}{space 2}-.0461707{col 34}{space 2} .3163308{col 45}{space 1}   -0.15{col 54}{space 3}0.884{col 62}{space 4}-.6661676{col 75}{space 3} .5738262
{txt}{space 17}age {c |}{col 22}{res}{space 2} -.002671{col 34}{space 2} .0125923{col 45}{space 1}   -0.21{col 54}{space 3}0.832{col 62}{space 4}-.0273514{col 75}{space 3} .0220094
{txt}{space 11}education {c |}{col 22}{res}{space 2}  .129773{col 34}{space 2} .1804035{col 45}{space 1}    0.72{col 54}{space 3}0.472{col 62}{space 4}-.2238113{col 75}{space 3} .4833574
{txt}{space 14}income {c |}{col 22}{res}{space 2} .1636383{col 34}{space 2}   .19473{col 45}{space 1}    0.84{col 54}{space 3}0.401{col 62}{space 4}-.2180254{col 75}{space 3} .5453021
{txt}{space 8}professional {c |}{col 22}{res}{space 2}-.3628738{col 34}{space 2}  .395446{col 45}{space 1}   -0.92{col 54}{space 3}0.359{col 62}{space 4}-1.137934{col 75}{space 3} .4121861
{txt}{space 13}laborer {c |}{col 22}{res}{space 2}-.0929292{col 34}{space 2} .4125265{col 45}{space 1}   -0.23{col 54}{space 3}0.822{col 62}{space 4}-.9014663{col 75}{space 3} .7156078
{txt}{space 10}unemployed {c |}{col 22}{res}{space 2}-.4830821{col 34}{space 2} .4478394{col 45}{space 1}   -1.08{col 54}{space 3}0.281{col 62}{space 4}-1.360831{col 75}{space 3}  .394667
{txt}{space 15}moved {c |}{col 22}{res}{space 2}-.3082328{col 34}{space 2} .3293959{col 45}{space 1}   -0.94{col 54}{space 3}0.349{col 62}{space 4}-.9538369{col 75}{space 3} .3373712
{txt}{space 15}_cons {c |}{col 22}{res}{space 2} 2.440845{col 34}{space 2} 1.491716{col 45}{space 1}    1.64{col 54}{space 3}0.102{col 62}{space 4}-.4828639{col 75}{space 3} 5.364554
{txt}{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}Long_term_detention  {txt}{c |}
{space 11}treatment {c |}{col 22}{res}{space 2}-2.858155{col 34}{space 2} .3540406{col 45}{space 1}   -8.07{col 54}{space 3}0.000{col 62}{space 4}-3.552062{col 75}{space 3}-2.164248
{txt}{space 7}revisisnoquit {c |}{col 22}{res}{space 2} .9313581{col 34}{space 2}  .220258{col 45}{space 1}    4.23{col 54}{space 3}0.000{col 62}{space 4} .4996603{col 75}{space 3} 1.363056
{txt}{space 5}revviolencejust {c |}{col 22}{res}{space 2} .2226935{col 34}{space 2} .1611356{col 45}{space 1}    1.38{col 54}{space 3}0.167{col 62}{space 4}-.0931265{col 75}{space 3} .5385135
{txt}{space 5}revwillofpeople {c |}{col 22}{res}{space 2}-.1684891{col 34}{space 2} .1463138{col 45}{space 1}   -1.15{col 54}{space 3}0.250{col 62}{space 4}-.4552588{col 75}{space 3} .1182807
{txt}{space 9}revblameciv {c |}{col 22}{res}{space 2}-.7758229{col 34}{space 2} .1710254{col 45}{space 1}   -4.54{col 54}{space 3}0.000{col 62}{space 4}-1.111027{col 75}{space 3}-.4406192
{txt}{space 10}isisvictim {c |}{col 22}{res}{space 2} .1310063{col 34}{space 2} .1793435{col 45}{space 1}    0.73{col 54}{space 3}0.465{col 62}{space 4}-.2205005{col 75}{space 3} .4825131
{txt}{space 14}female {c |}{col 22}{res}{space 2}-.2188388{col 34}{space 2} .3435912{col 45}{space 1}   -0.64{col 54}{space 3}0.524{col 62}{space 4}-.8922652{col 75}{space 3} .4545876
{txt}{space 17}age {c |}{col 22}{res}{space 2}-.0009291{col 34}{space 2} .0131464{col 45}{space 1}   -0.07{col 54}{space 3}0.944{col 62}{space 4}-.0266957{col 75}{space 3} .0248374
{txt}{space 11}education {c |}{col 22}{res}{space 2}-.3815992{col 34}{space 2} .1773013{col 45}{space 1}   -2.15{col 54}{space 3}0.031{col 62}{space 4}-.7291035{col 75}{space 3} -.034095
{txt}{space 14}income {c |}{col 22}{res}{space 2} .0251584{col 34}{space 2} .2014265{col 45}{space 1}    0.12{col 54}{space 3}0.901{col 62}{space 4}-.3696303{col 75}{space 3} .4199471
{txt}{space 8}professional {c |}{col 22}{res}{space 2}-.6592476{col 34}{space 2} .4392057{col 45}{space 1}   -1.50{col 54}{space 3}0.133{col 62}{space 4}-1.520075{col 75}{space 3} .2015798
{txt}{space 13}laborer {c |}{col 22}{res}{space 2}-.3871547{col 34}{space 2} .4275372{col 45}{space 1}   -0.91{col 54}{space 3}0.365{col 62}{space 4}-1.225112{col 75}{space 3} .4508029
{txt}{space 10}unemployed {c |}{col 22}{res}{space 2} .1873087{col 34}{space 2} .4577467{col 45}{space 1}    0.41{col 54}{space 3}0.682{col 62}{space 4}-.7098583{col 75}{space 3} 1.084476
{txt}{space 15}moved {c |}{col 22}{res}{space 2}-.3093833{col 34}{space 2}  .306935{col 45}{space 1}   -1.01{col 54}{space 3}0.313{col 62}{space 4}-.9109648{col 75}{space 3} .2921982
{txt}{space 15}_cons {c |}{col 22}{res}{space 2} 5.374268{col 34}{space 2} 1.615878{col 45}{space 1}    3.33{col 54}{space 3}0.001{col 62}{space 4} 2.207205{col 75}{space 3} 8.541331
{txt}{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}Life_sentence        {txt}{c |}
{space 11}treatment {c |}{col 22}{res}{space 2}-3.137203{col 34}{space 2} .4663348{col 45}{space 1}   -6.73{col 54}{space 3}0.000{col 62}{space 4}-4.051202{col 75}{space 3}-2.223203
{txt}{space 7}revisisnoquit {c |}{col 22}{res}{space 2} 1.139418{col 34}{space 2} .2822815{col 45}{space 1}    4.04{col 54}{space 3}0.000{col 62}{space 4} .5861568{col 75}{space 3}  1.69268
{txt}{space 5}revviolencejust {c |}{col 22}{res}{space 2}-.0343408{col 34}{space 2} .2218745{col 45}{space 1}   -0.15{col 54}{space 3}0.877{col 62}{space 4}-.4692067{col 75}{space 3} .4005252
{txt}{space 5}revwillofpeople {c |}{col 22}{res}{space 2}-.3687733{col 34}{space 2}  .202122{col 45}{space 1}   -1.82{col 54}{space 3}0.068{col 62}{space 4}-.7649252{col 75}{space 3} .0273786
{txt}{space 9}revblameciv {c |}{col 22}{res}{space 2}-1.378913{col 34}{space 2} .2272087{col 45}{space 1}   -6.07{col 54}{space 3}0.000{col 62}{space 4}-1.824234{col 75}{space 3}-.9335922
{txt}{space 10}isisvictim {c |}{col 22}{res}{space 2}-.3346628{col 34}{space 2} .2395377{col 45}{space 1}   -1.40{col 54}{space 3}0.162{col 62}{space 4} -.804148{col 75}{space 3} .1348224
{txt}{space 14}female {c |}{col 22}{res}{space 2} .0899102{col 34}{space 2} .4080863{col 45}{space 1}    0.22{col 54}{space 3}0.826{col 62}{space 4}-.7099242{col 75}{space 3} .8897445
{txt}{space 17}age {c |}{col 22}{res}{space 2}-.0262423{col 34}{space 2} .0156601{col 45}{space 1}   -1.68{col 54}{space 3}0.094{col 62}{space 4}-.0569355{col 75}{space 3} .0044508
{txt}{space 11}education {c |}{col 22}{res}{space 2}-.0240822{col 34}{space 2} .2239238{col 45}{space 1}   -0.11{col 54}{space 3}0.914{col 62}{space 4}-.4629648{col 75}{space 3} .4148004
{txt}{space 14}income {c |}{col 22}{res}{space 2} .4959011{col 34}{space 2} .2888698{col 45}{space 1}    1.72{col 54}{space 3}0.086{col 62}{space 4}-.0702734{col 75}{space 3} 1.062076
{txt}{space 8}professional {c |}{col 22}{res}{space 2}-.7090463{col 34}{space 2} .6601078{col 45}{space 1}   -1.07{col 54}{space 3}0.283{col 62}{space 4}-2.002834{col 75}{space 3} .5847412
{txt}{space 13}laborer {c |}{col 22}{res}{space 2}-.0990513{col 34}{space 2}   .55375{col 45}{space 1}   -0.18{col 54}{space 3}0.858{col 62}{space 4}-1.184381{col 75}{space 3} .9862787
{txt}{space 10}unemployed {c |}{col 22}{res}{space 2}  .522144{col 34}{space 2}  .523302{col 45}{space 1}    1.00{col 54}{space 3}0.318{col 62}{space 4}-.5035091{col 75}{space 3} 1.547797
{txt}{space 15}moved {c |}{col 22}{res}{space 2}-.3099933{col 34}{space 2} .4481544{col 45}{space 1}   -0.69{col 54}{space 3}0.489{col 62}{space 4} -1.18836{col 75}{space 3} .5683731
{txt}{space 15}_cons {c |}{col 22}{res}{space 2} 5.824977{col 34}{space 2} 2.005491{col 45}{space 1}    2.90{col 54}{space 3}0.004{col 62}{space 4} 1.894287{col 75}{space 3} 9.755667
{txt}{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}Death_penalty        {txt}{c |}
{space 11}treatment {c |}{col 22}{res}{space 2}-2.905968{col 34}{space 2} .5387572{col 45}{space 1}   -5.39{col 54}{space 3}0.000{col 62}{space 4}-3.961913{col 75}{space 3}-1.850023
{txt}{space 7}revisisnoquit {c |}{col 22}{res}{space 2} 1.491126{col 34}{space 2} .4523573{col 45}{space 1}    3.30{col 54}{space 3}0.001{col 62}{space 4} .6045224{col 75}{space 3}  2.37773
{txt}{space 5}revviolencejust {c |}{col 22}{res}{space 2}-.3569643{col 34}{space 2} .2915625{col 45}{space 1}   -1.22{col 54}{space 3}0.221{col 62}{space 4}-.9284163{col 75}{space 3} .2144877
{txt}{space 5}revwillofpeople {c |}{col 22}{res}{space 2}-1.433461{col 34}{space 2} .3228627{col 45}{space 1}   -4.44{col 54}{space 3}0.000{col 62}{space 4}-2.066261{col 75}{space 3}-.8006622
{txt}{space 9}revblameciv {c |}{col 22}{res}{space 2}-1.403281{col 34}{space 2} .2735596{col 45}{space 1}   -5.13{col 54}{space 3}0.000{col 62}{space 4}-1.939448{col 75}{space 3}-.8671136
{txt}{space 10}isisvictim {c |}{col 22}{res}{space 2}-.0942266{col 34}{space 2} .3038882{col 45}{space 1}   -0.31{col 54}{space 3}0.757{col 62}{space 4}-.6898366{col 75}{space 3} .5013834
{txt}{space 14}female {c |}{col 22}{res}{space 2}-.3364144{col 34}{space 2} .6058624{col 45}{space 1}   -0.56{col 54}{space 3}0.579{col 62}{space 4}-1.523883{col 75}{space 3} .8510542
{txt}{space 17}age {c |}{col 22}{res}{space 2}-.0333146{col 34}{space 2} .0239311{col 45}{space 1}   -1.39{col 54}{space 3}0.164{col 62}{space 4}-.0802188{col 75}{space 3} .0135895
{txt}{space 11}education {c |}{col 22}{res}{space 2}  .437303{col 34}{space 2} .2559831{col 45}{space 1}    1.71{col 54}{space 3}0.088{col 62}{space 4}-.0644147{col 75}{space 3} .9390208
{txt}{space 14}income {c |}{col 22}{res}{space 2}-.2033701{col 34}{space 2} .2900063{col 45}{space 1}   -0.70{col 54}{space 3}0.483{col 62}{space 4}-.7717721{col 75}{space 3} .3650319
{txt}{space 8}professional {c |}{col 22}{res}{space 2} .6900766{col 34}{space 2} .7883688{col 45}{space 1}    0.88{col 54}{space 3}0.381{col 62}{space 4}-.8550979{col 75}{space 3} 2.235251
{txt}{space 13}laborer {c |}{col 22}{res}{space 2} 1.072259{col 34}{space 2} .7219785{col 45}{space 1}    1.49{col 54}{space 3}0.137{col 62}{space 4}-.3427923{col 75}{space 3} 2.487311
{txt}{space 10}unemployed {c |}{col 22}{res}{space 2}-.0862892{col 34}{space 2} 1.343721{col 45}{space 1}   -0.06{col 54}{space 3}0.949{col 62}{space 4}-2.719934{col 75}{space 3} 2.547355
{txt}{space 15}moved {c |}{col 22}{res}{space 2}-.5335404{col 34}{space 2} .5626675{col 45}{space 1}   -0.95{col 54}{space 3}0.343{col 62}{space 4}-1.636349{col 75}{space 3} .5692677
{txt}{space 15}_cons {c |}{col 22}{res}{space 2} 6.999164{col 34}{space 2} 2.475974{col 45}{space 1}    2.83{col 54}{space 3}0.005{col 62}{space 4} 2.146344{col 75}{space 3} 11.85198
{txt}{hline 21}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. 
. *Punishment for Extrajudicial Killing (Logit regression)
. 
. logit drevthreat treatment, robust

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-400.03297}  
Iteration 1:{space 3}log pseudolikelihood = {res:-390.75617}  
Iteration 2:{space 3}log pseudolikelihood = {res:-390.74579}  
Iteration 3:{space 3}log pseudolikelihood = {res:-390.74579}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}       595
{txt}{col 49}Wald chi2({res}1{txt}){col 67}= {res}     18.33
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-390.74579{txt}{col 49}Pseudo R2{col 67}= {res}    0.0232

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}  drevthreat{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}treatment {c |}{col 14}{res}{space 2}-.7325396{col 26}{space 2} .1710836{col 37}{space 1}   -4.28{col 46}{space 3}0.000{col 54}{space 4}-1.067857{col 67}{space 3}-.3972218
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .7325396{col 26}{space 2} .2781194{col 37}{space 1}    2.63{col 46}{space 3}0.008{col 54}{space 4} .1874355{col 67}{space 3} 1.277644
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. logit drevthreat treatment mosul, robust

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-400.03297}  
Iteration 1:{space 3}log pseudolikelihood = {res:-265.70864}  
Iteration 2:{space 3}log pseudolikelihood = {res:-260.72552}  
Iteration 3:{space 3}log pseudolikelihood = {res:-260.55851}  
Iteration 4:{space 3}log pseudolikelihood = {res:-260.55803}  
Iteration 5:{space 3}log pseudolikelihood = {res:-260.55803}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}       595
{txt}{col 49}Wald chi2({res}2{txt}){col 67}= {res}    107.17
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-260.55803{txt}{col 49}Pseudo R2{col 67}= {res}    0.3487

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}  drevthreat{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}treatment {c |}{col 14}{res}{space 2}  -2.6245{col 26}{space 2} .3705274{col 37}{space 1}   -7.08{col 46}{space 3}0.000{col 54}{space 4} -3.35072{col 67}{space 3} -1.89828
{txt}{space 7}mosul {c |}{col 14}{res}{space 2}-4.040198{col 26}{space 2} .3989942{col 37}{space 1}  -10.13{col 46}{space 3}0.000{col 54}{space 4}-4.822212{col 67}{space 3}-3.258184
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 6.183296{col 26}{space 2} .8007431{col 37}{space 1}    7.72{col 46}{space 3}0.000{col 54}{space 4} 4.613869{col 67}{space 3} 7.752724
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. logit drevthreat treatment revisisnoquit revviolencejust revwillofpeople revblameciv isisvictim female age education income professional laborer unemployed moved, robust

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-397.25998}  
Iteration 1:{space 3}log pseudolikelihood = {res:-292.77414}  
Iteration 2:{space 3}log pseudolikelihood = {res:-291.12013}  
Iteration 3:{space 3}log pseudolikelihood = {res:-291.11624}  
Iteration 4:{space 3}log pseudolikelihood = {res:-291.11624}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}       592
{txt}{col 49}Wald chi2({res}14{txt}){col 67}= {res}    149.08
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-291.11624{txt}{col 49}Pseudo R2{col 67}= {res}    0.2672

{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}    Robust
{col 1}     drevthreat{col 17}{c |}      Coef.{col 29}   Std. Err.{col 41}      z{col 49}   P>|z|{col 57}     [95% Con{col 70}f. Interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}treatment {c |}{col 17}{res}{space 2}-1.831018{col 29}{space 2} .2437188{col 40}{space 1}   -7.51{col 49}{space 3}0.000{col 57}{space 4}-2.308698{col 70}{space 3}-1.353338
{txt}{space 2}revisisnoquit {c |}{col 17}{res}{space 2} .7539557{col 29}{space 2} .1590654{col 40}{space 1}    4.74{col 49}{space 3}0.000{col 57}{space 4} .4421931{col 70}{space 3} 1.065718
{txt}revviolencejust {c |}{col 17}{res}{space 2}-.0850005{col 29}{space 2}  .123667{col 40}{space 1}   -0.69{col 49}{space 3}0.492{col 57}{space 4}-.3273833{col 70}{space 3} .1573823
{txt}revwillofpeople {c |}{col 17}{res}{space 2}-.3506146{col 29}{space 2} .1089112{col 40}{space 1}   -3.22{col 49}{space 3}0.001{col 57}{space 4}-.5640767{col 70}{space 3}-.1371526
{txt}{space 4}revblameciv {c |}{col 17}{res}{space 2}-.7164665{col 29}{space 2} .1196785{col 40}{space 1}   -5.99{col 49}{space 3}0.000{col 57}{space 4}-.9510319{col 70}{space 3} -.481901
{txt}{space 5}isisvictim {c |}{col 17}{res}{space 2} .0174498{col 29}{space 2} .1412344{col 40}{space 1}    0.12{col 49}{space 3}0.902{col 57}{space 4}-.2593646{col 70}{space 3} .2942642
{txt}{space 9}female {c |}{col 17}{res}{space 2}-.1183154{col 29}{space 2}  .271555{col 40}{space 1}   -0.44{col 49}{space 3}0.663{col 57}{space 4}-.6505534{col 70}{space 3} .4139225
{txt}{space 12}age {c |}{col 17}{res}{space 2}-.0086134{col 29}{space 2} .0100815{col 40}{space 1}   -0.85{col 49}{space 3}0.393{col 57}{space 4}-.0283728{col 70}{space 3}  .011146
{txt}{space 6}education {c |}{col 17}{res}{space 2}-.2922039{col 29}{space 2} .1414401{col 40}{space 1}   -2.07{col 49}{space 3}0.039{col 57}{space 4}-.5694214{col 70}{space 3}-.0149864
{txt}{space 9}income {c |}{col 17}{res}{space 2} -.017878{col 29}{space 2} .1570889{col 40}{space 1}   -0.11{col 49}{space 3}0.909{col 57}{space 4}-.3257665{col 70}{space 3} .2900106
{txt}{space 3}professional {c |}{col 17}{res}{space 2}-.2994541{col 29}{space 2}  .361785{col 40}{space 1}   -0.83{col 49}{space 3}0.408{col 57}{space 4} -1.00854{col 70}{space 3} .4096315
{txt}{space 8}laborer {c |}{col 17}{res}{space 2} -.135138{col 29}{space 2} .3539295{col 40}{space 1}   -0.38{col 49}{space 3}0.703{col 57}{space 4} -.828827{col 70}{space 3} .5585511
{txt}{space 5}unemployed {c |}{col 17}{res}{space 2} .4809656{col 29}{space 2}  .350273{col 40}{space 1}    1.37{col 49}{space 3}0.170{col 57}{space 4}-.2055569{col 70}{space 3} 1.167488
{txt}{space 10}moved {c |}{col 17}{res}{space 2}-.2229777{col 29}{space 2} .2446192{col 40}{space 1}   -0.91{col 49}{space 3}0.362{col 57}{space 4}-.7024225{col 70}{space 3} .2564672
{txt}{space 10}_cons {c |}{col 17}{res}{space 2} 5.023562{col 29}{space 2} 1.173399{col 40}{space 1}    4.28{col 49}{space 3}0.000{col 57}{space 4} 2.723742{col 70}{space 3} 7.323381
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. *Manuscript Table 2 Robustness Checks
. 
. reg revalphacapsur treatment, robust

{txt}Linear regression                               Number of obs     = {res}       595
                                                {txt}F(1, 593)         =  {res}   153.31
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.2050
                                                {txt}Root MSE          =    {res} .82463

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}revalphaca~r{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}treatment {c |}{col 14}{res}{space 2}-.8469275{col 26}{space 2} .0684017{col 37}{space 1}  -12.38{col 46}{space 3}0.000{col 54}{space 4}-.9812666{col 67}{space 3}-.7125885
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 4.208928{col 26}{space 2} .1129658{col 37}{space 1}   37.26{col 46}{space 3}0.000{col 54}{space 4} 3.987066{col 67}{space 3} 4.430789
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg revrightcapsur treatment, robust

{txt}Linear regression                               Number of obs     = {res}       591
                                                {txt}F(1, 589)         =  {res}   109.82
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.1535
                                                {txt}Root MSE          =    {res} 1.1606

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}revrightca~r{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}treatment {c |}{col 14}{res}{space 2}-.9995885{col 26}{space 2} .0953869{col 37}{space 1}  -10.48{col 46}{space 3}0.000{col 54}{space 4}-1.186928{col 67}{space 3}-.8122487
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 4.366524{col 26}{space 2} .1541506{col 37}{space 1}   28.33{col 46}{space 3}0.000{col 54}{space 4} 4.063772{col 67}{space 3} 4.669276
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. reg revalphacapsur treatment mosul, robust

{txt}Linear regression                               Number of obs     = {res}       595
                                                {txt}F(2, 592)         =  {res}   363.09
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.4384
                                                {txt}Root MSE          =    {res} .69365

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}revalphaca~r{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}treatment {c |}{col 14}{res}{space 2}-.6344057{col 26}{space 2} .0612274{col 37}{space 1}  -10.36{col 46}{space 3}0.000{col 54}{space 4} -.754655{col 67}{space 3}-.5141564
{txt}{space 7}mosul {c |}{col 14}{res}{space 2} .9776004{col 26}{space 2} .0610803{col 37}{space 1}   16.01{col 46}{space 3}0.000{col 54}{space 4} .8576399{col 67}{space 3} 1.097561
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 3.214325{col 26}{space 2} .1173446{col 37}{space 1}   27.39{col 46}{space 3}0.000{col 54}{space 4} 2.983863{col 67}{space 3} 3.444788
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg revrightcapsur treatment mosul, robust

{txt}Linear regression                               Number of obs     = {res}       591
                                                {txt}F(2, 588)         =  {res}   185.66
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.3524
                                                {txt}Root MSE          =    {res} 1.0159

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}revrightca~r{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}treatment {c |}{col 14}{res}{space 2} -.726973{col 26}{space 2} .0875001{col 37}{space 1}   -8.31{col 46}{space 3}0.000{col 54}{space 4}-.8988237{col 67}{space 3}-.5551223
{txt}{space 7}mosul {c |}{col 14}{res}{space 2} 1.236656{col 26}{space 2} .0993654{col 37}{space 1}   12.45{col 46}{space 3}0.000{col 54}{space 4} 1.041501{col 67}{space 3}  1.43181
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 3.096605{col 26}{space 2} .1744335{col 37}{space 1}   17.75{col 46}{space 3}0.000{col 54}{space 4} 2.754017{col 67}{space 3} 3.439194
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. reg revalphacapsur treatment revisisnoquit revbiasedisis revdeathpenjust revblameciv isisvictim female age education income professional laborer unemployed moved, robust

{txt}Linear regression                               Number of obs     = {res}       591
                                                {txt}F(14, 576)        =  {res}    72.46
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.4914
                                                {txt}Root MSE          =    {res} .66826

{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}    Robust
{col 1} revalphacapsur{col 17}{c |}      Coef.{col 29}   Std. Err.{col 41}      t{col 49}   P>|t|{col 57}     [95% Con{col 70}f. Interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}treatment {c |}{col 17}{res}{space 2}-.5755207{col 29}{space 2}  .064232{col 40}{space 1}   -8.96{col 49}{space 3}0.000{col 57}{space 4}-.7016783{col 70}{space 3}-.4493631
{txt}{space 2}revisisnoquit {c |}{col 17}{res}{space 2}-.2752834{col 29}{space 2} .0444774{col 40}{space 1}   -6.19{col 49}{space 3}0.000{col 57}{space 4} -.362641{col 70}{space 3}-.1879257
{txt}{space 2}revbiasedisis {c |}{col 17}{res}{space 2}-.1534718{col 29}{space 2} .0384887{col 40}{space 1}   -3.99{col 49}{space 3}0.000{col 57}{space 4}-.2290671{col 70}{space 3}-.0778764
{txt}revdeathpenjust {c |}{col 17}{res}{space 2}  .072789{col 29}{space 2} .0290877{col 40}{space 1}    2.50{col 49}{space 3}0.013{col 57}{space 4} .0156581{col 70}{space 3} .1299198
{txt}{space 4}revblameciv {c |}{col 17}{res}{space 2} .0246098{col 29}{space 2} .0313489{col 40}{space 1}    0.79{col 49}{space 3}0.433{col 57}{space 4}-.0369622{col 70}{space 3} .0861819
{txt}{space 5}isisvictim {c |}{col 17}{res}{space 2} .0142391{col 29}{space 2} .0414713{col 40}{space 1}    0.34{col 49}{space 3}0.731{col 57}{space 4}-.0672143{col 70}{space 3} .0956924
{txt}{space 9}female {c |}{col 17}{res}{space 2}-.0853125{col 29}{space 2} .0763153{col 40}{space 1}   -1.12{col 49}{space 3}0.264{col 57}{space 4}-.2352026{col 70}{space 3} .0645776
{txt}{space 12}age {c |}{col 17}{res}{space 2} .0056951{col 29}{space 2} .0026016{col 40}{space 1}    2.19{col 49}{space 3}0.029{col 57}{space 4} .0005852{col 70}{space 3} .0108049
{txt}{space 6}education {c |}{col 17}{res}{space 2}-.0152082{col 29}{space 2} .0360966{col 40}{space 1}   -0.42{col 49}{space 3}0.674{col 57}{space 4}-.0861053{col 70}{space 3} .0556889
{txt}{space 9}income {c |}{col 17}{res}{space 2} .1509056{col 29}{space 2} .0399301{col 40}{space 1}    3.78{col 49}{space 3}0.000{col 57}{space 4} .0724793{col 70}{space 3}  .229332
{txt}{space 3}professional {c |}{col 17}{res}{space 2} .0425346{col 29}{space 2} .0977438{col 40}{space 1}    0.44{col 49}{space 3}0.664{col 57}{space 4}-.1494432{col 70}{space 3} .2345123
{txt}{space 8}laborer {c |}{col 17}{res}{space 2}-.0789839{col 29}{space 2} .0960248{col 40}{space 1}   -0.82{col 49}{space 3}0.411{col 57}{space 4}-.2675854{col 70}{space 3} .1096177
{txt}{space 5}unemployed {c |}{col 17}{res}{space 2} .1010706{col 29}{space 2} .0968398{col 40}{space 1}    1.04{col 49}{space 3}0.297{col 57}{space 4}-.0891315{col 70}{space 3} .2912728
{txt}{space 10}moved {c |}{col 17}{res}{space 2} .0036211{col 29}{space 2} .0748689{col 40}{space 1}    0.05{col 49}{space 3}0.961{col 57}{space 4}-.1434282{col 70}{space 3} .1506705
{txt}{space 10}_cons {c |}{col 17}{res}{space 2} 4.040953{col 29}{space 2} .2631993{col 40}{space 1}   15.35{col 49}{space 3}0.000{col 57}{space 4} 3.524005{col 70}{space 3}   4.5579
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg revrightcapsur treatment revisisnoquit revbiasedisis revdeathpenjust revblameciv isisvictim female age education income professional laborer unemployed moved, robust

{txt}Linear regression                               Number of obs     = {res}       587
                                                {txt}F(14, 572)        =  {res}    46.06
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.4273
                                                {txt}Root MSE          =    {res} .96823

{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}    Robust
{col 1} revrightcapsur{col 17}{c |}      Coef.{col 29}   Std. Err.{col 41}      t{col 49}   P>|t|{col 57}     [95% Con{col 70}f. Interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}treatment {c |}{col 17}{res}{space 2}-.6708408{col 29}{space 2} .0893253{col 40}{space 1}   -7.51{col 49}{space 3}0.000{col 57}{space 4}-.8462864{col 70}{space 3}-.4953953
{txt}{space 2}revisisnoquit {c |}{col 17}{res}{space 2}-.4117225{col 29}{space 2} .0605774{col 40}{space 1}   -6.80{col 49}{space 3}0.000{col 57}{space 4}-.5307037{col 70}{space 3}-.2927412
{txt}{space 2}revbiasedisis {c |}{col 17}{res}{space 2}-.2545646{col 29}{space 2} .0549652{col 40}{space 1}   -4.63{col 49}{space 3}0.000{col 57}{space 4}-.3625228{col 70}{space 3}-.1466063
{txt}revdeathpenjust {c |}{col 17}{res}{space 2} .0252992{col 29}{space 2}  .043863{col 40}{space 1}    0.58{col 49}{space 3}0.564{col 57}{space 4}-.0608529{col 70}{space 3} .1114513
{txt}{space 4}revblameciv {c |}{col 17}{res}{space 2}-.0031806{col 29}{space 2} .0443843{col 40}{space 1}   -0.07{col 49}{space 3}0.943{col 57}{space 4}-.0903566{col 70}{space 3} .0839955
{txt}{space 5}isisvictim {c |}{col 17}{res}{space 2} .0060901{col 29}{space 2} .0599133{col 40}{space 1}    0.10{col 49}{space 3}0.919{col 57}{space 4}-.1115868{col 70}{space 3} .1237669
{txt}{space 9}female {c |}{col 17}{res}{space 2}  -.13847{col 29}{space 2} .1148248{col 40}{space 1}   -1.21{col 49}{space 3}0.228{col 57}{space 4}-.3639997{col 70}{space 3} .0870597
{txt}{space 12}age {c |}{col 17}{res}{space 2} .0051958{col 29}{space 2} .0036648{col 40}{space 1}    1.42{col 49}{space 3}0.157{col 57}{space 4}-.0020023{col 70}{space 3} .0123939
{txt}{space 6}education {c |}{col 17}{res}{space 2}-.0310265{col 29}{space 2}  .055753{col 40}{space 1}   -0.56{col 49}{space 3}0.578{col 57}{space 4}-.1405322{col 70}{space 3} .0784791
{txt}{space 9}income {c |}{col 17}{res}{space 2}    .2245{col 29}{space 2} .0576812{col 40}{space 1}    3.89{col 49}{space 3}0.000{col 57}{space 4} .1112072{col 70}{space 3} .3377927
{txt}{space 3}professional {c |}{col 17}{res}{space 2}-.0171392{col 29}{space 2} .1396147{col 40}{space 1}   -0.12{col 49}{space 3}0.902{col 57}{space 4}-.2913592{col 70}{space 3} .2570808
{txt}{space 8}laborer {c |}{col 17}{res}{space 2}-.0253357{col 29}{space 2} .1486251{col 40}{space 1}   -0.17{col 49}{space 3}0.865{col 57}{space 4}-.3172532{col 70}{space 3} .2665817
{txt}{space 5}unemployed {c |}{col 17}{res}{space 2} .1472036{col 29}{space 2} .1453967{col 40}{space 1}    1.01{col 49}{space 3}0.312{col 57}{space 4} -.138373{col 70}{space 3} .4327802
{txt}{space 10}moved {c |}{col 17}{res}{space 2} .0168161{col 29}{space 2} .1035418{col 40}{space 1}    0.16{col 49}{space 3}0.871{col 57}{space 4}-.1865525{col 70}{space 3} .2201846
{txt}{space 10}_cons {c |}{col 17}{res}{space 2} 4.804767{col 29}{space 2} .3609129{col 40}{space 1}   13.31{col 49}{space 3}0.000{col 57}{space 4}  4.09589{col 70}{space 3} 5.513643
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. log close
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\swhitt\Desktop\Mosul\Extrajudicial Killing Mosul\ISQ\Final\ISQ Extrajudicial replication logfile.smcl
  {txt}log type:  {res}smcl
 {txt}closed on:  {res}14 Feb 2021, 12:31:43
{txt}{.-}
{smcl}
{txt}{sf}{ul off}